Operational modes and topological changes affect power flow in the power systems. As a result, a broad spectrum of protection issues may happen in the power system. So, both the operational and topological changes should be detected fast to prevent blackouts. On the other hand, the existing detection schemes are complex in analyzing and implementation. Therefore, there is a need for an online scheme to identify the network's topology and operation mode simultaneously without complex computations and additional communication infrastructures. To this end, a comprehensive scheme is proposed in which the changes are detected by analyzing the power flow obtained from the network. For this purpose, line outage contingencies and operation modes are defined in rules to be used in a fuzzy inference system (FIS) as a decision-making tool. The proposed scheme can be implemented on existing lines as a communication infrastructure and determines the network’s status in an online manner. Also, in comparison to the existing schemes, the proposed scheme reduces the complexity and the computational burden. The proposed scheme is implemented on IEEE 8-bus system and the results proved its efficiency.
Arc Fault and Flash Signal Analysis and Detection in DC Distribution Systems ...IRJET Journal
This document summarizes a research paper that proposes a new approach for detecting arc faults and flashes in DC distribution systems using wavelet transform and fuzzy logic. The researchers designed a model of an arc condition and analyzed the arc voltage using wavelet transform. Wavelet analysis was able to extract features of the signal that were then used to design a fuzzy rule base. This approach allows for arc fault detection by analyzing features in the PV output voltage. The full system was implemented and tested in MATLAB. The proposed method uses wavelet transform for spectral energy calibration of arc faults, which provides a more detectable signal signature compared to other techniques. This allows for accurate arc fault analysis and classification in DC systems.
Fuzzy-Logic-Controller-Based Fault Isolation in PWM VSI for Vector Controlled...iosrjce
IOSR Journal of Electrical and Electronics Engineering(IOSR-JEEE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electrical and electronics engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electrical and electronics engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Differential equation fault location algorithm with harmonic effects in power...TELKOMNIKA JOURNAL
About 80% of faults in the power system distribution are earth faults. Studies to find effective methods to identify and locate faults in distribution networks are still relevant, in addition to the presence of harmonic signals that distort waves and create deviations in the power system that can cause many problems to the protection relay. This study focuses on a single line-to-ground (SLG) fault location algorithm in a power system distribution network based on fundamental frequency measured using the differential equation method. The developed algorithm considers the presence of harmonics components in the simulation network. In this study, several filters were tested to obtain the lowest fault location error to reduce the effect of harmonic components on the developed fault location algorithm. The network model is simulated using the alternate transients program (ATP)Draw simulation program. Several fault scenarios have been implemented during the simulation, such as fault resistance, fault distance, and fault inception angle. The final results show that the proposed algorithm can estimate the fault distance successfully with an acceptable fault location error. Based on the simulation results, the differential equation continuous wavelet technique (CWT) filter-based algorithm produced an accurate fault location result with a mean average error (MAE) of less than 5%.
This document presents a fuzzy logic controller-based method for isolating open circuit faults in a pulse width modulated voltage source inverter (PWM VSI) that provides power to a vector controlled induction motor drive. The method uses distortions in current waveforms that occur during faulty operating stages of the inverter to detect and isolate faults. Simulation results show the effectiveness of the proposed fuzzy logic controller in isolating open circuit faults introduced in the PWM VSI. The controller is able to isolate faults without additional hardware and with low computational requirements, making it suitable for implementation in existing vector controlled induction motor drive systems.
Enhanced two-terminal impedance-based fault location using sequence valuesIJECEIAES
Fault at transmission line system may lead to major impacts such as power quality problems and cascading failure in the grid system. Thus, it is very important to locate it fast so that suitable solution can be taken to ensure power system stability can be retained. The complexity of the transmission line however makes the fault point identification a challenging task. This paper proposes an enhanced fault detection and location method using positive and negative-sequence values of current and voltage, taken at both local and remote terminals. The fault detection is based on comparison between the total fault current with currents combination during the pre-fault time. While the fault location algorithm was developed using an impedancebased method and the estimated fault location was taken at two cycles after fault detection. Various fault types, fault resistances and fault locations have been tested in order to verify the performance of the proposed method. The developed algorithms have successfully detected all faults within high accuracy. Based on the obtained results, the estimated fault locations are not affected by fault resistance and line charging current. Furthermore, the proposed method able to detect fault location without the needs to know the fault type.
"Use of PMU data for locating faults and mitigating cascading outage"Power System Operation
This document summarizes two methods presented in the paper: 1) A fault location method that uses sparse PMU data and electromechanical wave propagation to detect faults on transmission lines. It introduces a decision tree classifier to analyze voltage measurements and locate faults with high accuracy. 2) A controlled islanding scheme to predict and mitigate cascading outages. It uses spectral clustering to partition the system and suggest switching actions to create stable islands with minimum load shedding. The methods were tested on simulated systems and show potential to improve grid monitoring, fault response and prevention of blackouts.
A Passive Islanding Detection Method for Neutral point clamped Multilevel Inv...IJECEIAES
Presently renewable energies have taken a special place in the world and most of the Distributed Generations (DGs) used in the interconnected power system are utilized, renewable energy resources. Due to the DG‟s advantages, including use of renewable energy such as, clean nature, does not pollute environment and having endless nature the use of these renewable resources to produce electrical energy in the world are increasing in day to day life. One problem with such Distributed generators is an unintentional islanding phenomenon. Islanding occurs when a Distributed Generation continues to energize an isolated part of a power system even after it was disconnected from the main grid, which is surrounded by unpowered lines. Since islanding can cause hazardous conditions for people and equipment which is connected to it. As per IEEE 1547 DG Interconnection standards, islanding should be quickly detected within 2 seconds, by protective relays and inverters that are part of the DG system. In this paper, a new passive method to identify islanding states has been proposed, based on the rate of change of frequency analysis (ROCOF) for a multilevel inverter based solar distributed generation systems. This method is efficient for both connecting DGs to the network with or without the Inverter. This method is more efficient than the existing methods and reducing the Non Detection Zone (NDZ), which is the disadvantage of existing passive methods and also clearly differentiating between the Islanding and Non-islanding events. The simulation results, which are carried on the MATLAB/Simulink environment shows the performance of the proposed method
An accurate technique for supervising distance relays during power swingnooriasukmaningtyas
The document proposes an accurate technique for supervising distance relays during power swings. It constructs a locus diagram of the current and voltage differences between the two ends of a protected transmission line using synchronized phasor measurements. It then calculates the circumference of the constructed ellipse as an index to discriminate between faults and power swings in the first stage. If no fault is detected, it checks the power angle in the second stage to identify power swings. Simulation tests on a two-area four-machine power system demonstrate that the technique can accurately differentiate faults from power swings and ensure proper blocking/unblocking of distance relays.
Arc Fault and Flash Signal Analysis and Detection in DC Distribution Systems ...IRJET Journal
This document summarizes a research paper that proposes a new approach for detecting arc faults and flashes in DC distribution systems using wavelet transform and fuzzy logic. The researchers designed a model of an arc condition and analyzed the arc voltage using wavelet transform. Wavelet analysis was able to extract features of the signal that were then used to design a fuzzy rule base. This approach allows for arc fault detection by analyzing features in the PV output voltage. The full system was implemented and tested in MATLAB. The proposed method uses wavelet transform for spectral energy calibration of arc faults, which provides a more detectable signal signature compared to other techniques. This allows for accurate arc fault analysis and classification in DC systems.
Fuzzy-Logic-Controller-Based Fault Isolation in PWM VSI for Vector Controlled...iosrjce
IOSR Journal of Electrical and Electronics Engineering(IOSR-JEEE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electrical and electronics engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electrical and electronics engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Differential equation fault location algorithm with harmonic effects in power...TELKOMNIKA JOURNAL
About 80% of faults in the power system distribution are earth faults. Studies to find effective methods to identify and locate faults in distribution networks are still relevant, in addition to the presence of harmonic signals that distort waves and create deviations in the power system that can cause many problems to the protection relay. This study focuses on a single line-to-ground (SLG) fault location algorithm in a power system distribution network based on fundamental frequency measured using the differential equation method. The developed algorithm considers the presence of harmonics components in the simulation network. In this study, several filters were tested to obtain the lowest fault location error to reduce the effect of harmonic components on the developed fault location algorithm. The network model is simulated using the alternate transients program (ATP)Draw simulation program. Several fault scenarios have been implemented during the simulation, such as fault resistance, fault distance, and fault inception angle. The final results show that the proposed algorithm can estimate the fault distance successfully with an acceptable fault location error. Based on the simulation results, the differential equation continuous wavelet technique (CWT) filter-based algorithm produced an accurate fault location result with a mean average error (MAE) of less than 5%.
This document presents a fuzzy logic controller-based method for isolating open circuit faults in a pulse width modulated voltage source inverter (PWM VSI) that provides power to a vector controlled induction motor drive. The method uses distortions in current waveforms that occur during faulty operating stages of the inverter to detect and isolate faults. Simulation results show the effectiveness of the proposed fuzzy logic controller in isolating open circuit faults introduced in the PWM VSI. The controller is able to isolate faults without additional hardware and with low computational requirements, making it suitable for implementation in existing vector controlled induction motor drive systems.
Enhanced two-terminal impedance-based fault location using sequence valuesIJECEIAES
Fault at transmission line system may lead to major impacts such as power quality problems and cascading failure in the grid system. Thus, it is very important to locate it fast so that suitable solution can be taken to ensure power system stability can be retained. The complexity of the transmission line however makes the fault point identification a challenging task. This paper proposes an enhanced fault detection and location method using positive and negative-sequence values of current and voltage, taken at both local and remote terminals. The fault detection is based on comparison between the total fault current with currents combination during the pre-fault time. While the fault location algorithm was developed using an impedancebased method and the estimated fault location was taken at two cycles after fault detection. Various fault types, fault resistances and fault locations have been tested in order to verify the performance of the proposed method. The developed algorithms have successfully detected all faults within high accuracy. Based on the obtained results, the estimated fault locations are not affected by fault resistance and line charging current. Furthermore, the proposed method able to detect fault location without the needs to know the fault type.
"Use of PMU data for locating faults and mitigating cascading outage"Power System Operation
This document summarizes two methods presented in the paper: 1) A fault location method that uses sparse PMU data and electromechanical wave propagation to detect faults on transmission lines. It introduces a decision tree classifier to analyze voltage measurements and locate faults with high accuracy. 2) A controlled islanding scheme to predict and mitigate cascading outages. It uses spectral clustering to partition the system and suggest switching actions to create stable islands with minimum load shedding. The methods were tested on simulated systems and show potential to improve grid monitoring, fault response and prevention of blackouts.
A Passive Islanding Detection Method for Neutral point clamped Multilevel Inv...IJECEIAES
Presently renewable energies have taken a special place in the world and most of the Distributed Generations (DGs) used in the interconnected power system are utilized, renewable energy resources. Due to the DG‟s advantages, including use of renewable energy such as, clean nature, does not pollute environment and having endless nature the use of these renewable resources to produce electrical energy in the world are increasing in day to day life. One problem with such Distributed generators is an unintentional islanding phenomenon. Islanding occurs when a Distributed Generation continues to energize an isolated part of a power system even after it was disconnected from the main grid, which is surrounded by unpowered lines. Since islanding can cause hazardous conditions for people and equipment which is connected to it. As per IEEE 1547 DG Interconnection standards, islanding should be quickly detected within 2 seconds, by protective relays and inverters that are part of the DG system. In this paper, a new passive method to identify islanding states has been proposed, based on the rate of change of frequency analysis (ROCOF) for a multilevel inverter based solar distributed generation systems. This method is efficient for both connecting DGs to the network with or without the Inverter. This method is more efficient than the existing methods and reducing the Non Detection Zone (NDZ), which is the disadvantage of existing passive methods and also clearly differentiating between the Islanding and Non-islanding events. The simulation results, which are carried on the MATLAB/Simulink environment shows the performance of the proposed method
An accurate technique for supervising distance relays during power swingnooriasukmaningtyas
The document proposes an accurate technique for supervising distance relays during power swings. It constructs a locus diagram of the current and voltage differences between the two ends of a protected transmission line using synchronized phasor measurements. It then calculates the circumference of the constructed ellipse as an index to discriminate between faults and power swings in the first stage. If no fault is detected, it checks the power angle in the second stage to identify power swings. Simulation tests on a two-area four-machine power system demonstrate that the technique can accurately differentiate faults from power swings and ensure proper blocking/unblocking of distance relays.
This document summarizes a study that performed contingency analysis on Nigeria's 330kV power transmission network to identify vulnerabilities. Fast Decoupled Load Flow (FDLF) analysis was used to simulate the impact of single line outages. When the Kainji-GS line went down, several buses experienced low voltages and several lines saw power losses over 5%. The performance indices of each line were calculated and ranked based on their severity to identify the most critical lines.
This document summarizes a research paper that proposes a network-based coordination control technique for synchronizing a microgrid with the main electric power system during reconnection. A microgrid contains multiple distributed generators that must be controlled cooperatively to satisfy synchronization criteria. The proposed method uses a microgrid central controller that communicates with distributed generator controllers over a communication network. The controller calculates frequency, voltage, and phase offset commands to minimize differences between the microgrid and main grid. Simulation results using Matlab/Simulink show the technique can reliably synchronize the microgrid for network delays up to 200ms but not for delays of 500ms or greater.
Compared to a time-based maintenance schedule, condition-based
maintenance provides better diagnostic information on the health condition
of the different wind turbine components and subsystems. Rather than using
an offline condition monitoring technique, which require the WT to be taken
out of service, online condition monitoring does not require any interruption
on the WT operation. The online condition monitoring system uses different
types of sensors such as vibration, acoustic, temperature, current/voltage etc.
Using a machine learning approach, we aim to establish a data driven fault
prognosis framework. Instead of traditional wired communications, wireless
communication systems such as wireless sensor network have the advantages
of easier installation and lower capital cost. We propose the use of WSN for
collecting and transmitting the condition monitoring data to enhance the
reliability of wind parks. Using data driven approach the collective health of
the WP can be represented based on the condition of the individual wind
turbines, which can be used for predicting the remaining useful life of the
system.
Cetc11 - Wireless Magnetic Based Sensor System For Vehicles ClassificationNokia Networks
This paper presents a full pre-industrial prototype implementation of an innovative system capable to
detects vehicles and classify them. This work study the architecture of a full autonomous road sensor
network that count and classify passing vehicles, reducing the impact of conventional wired systems in
roads, by dramatically reducing their installation and operational costs. The power supply can be supported
either by solar harvest or by none rechargeable batteries. For communications the IEEE 802.15.4 with
ZigBee on top are used to transmit digitalized vehicle’s magnetic analogue signatures up to a vehicles
classification server. This information is acquired using a magnetometer translating in voltage the variation
of the natural magnetic field of the earth caused by ferromagnetic components of a vehicle passing over the
sensor. Results show clearly that it is possible to distinguish vehicles magnetic signatures per model and
class even at different speeds.
This document discusses research on measuring the sag of transmission lines using non-contact electric field sensing. It presents a study that uses finite element modeling to analyze the electric field distribution of conductors, establish relationships between the electric field and transmission line parameters, and propose a simplified sag calculation model. Simulation results assess the impact of transmission line parameters on sag measurements. Measurement schemes are designed for both single and dual-circuit transmission lines. The goal is to enable simple and efficient sag monitoring using electric field measurements from sensor arrays to help maintain power grid stability.
Transmission line is one the important compnent in protection of electric power system because the transmission line connects the power station with load centers.
The fault includes storms, lightning, snow, damage to insulation, short circuit fault [1].
Fault needs to be predicted earlier in order to be prevented before it occur
This document proposes using Particle Swarm Optimization (PSO) to detect faults on overhead transmission lines. It will involve simulating various fault types using PSCAD/EMTDC software, developing mathematical models of the voltage and current signals, and applying PSO in MATLAB to analyze the signals and detect faults. Preliminary results show simulation of five fault types. The developed method is expected to accurately detect faults before disturbances occur to maintain power system stability.
IRJET- Frequency-Based Energy Management in Islanded MicrogridIRJET Journal
1) The document discusses a frequency-based energy management system for an islanded microgrid.
2) It proposes using variations in grid frequency as a signal to coordinate energy production from renewable sources and energy storage as conditions change.
3) When battery is nearly full or solar production is high, frequency will rise and signal to curtail renewable production. When battery is low, frequency falls and loads can be shed.
A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Peri...IJECEIAES
This paper presents a diagnostic analytics of harmonic source signature recognition of rectifier and inverter-based load in the distribution system with single-point measurement at the point of common coupling by utilizing Periodogram. Signature recognition pattern is used to distinguish the harmonic sources accurately by obtaining the distribution of harmonic and interharmonic components and the harmonic contribution changes. This is achieved by using the significant signature recognition of harmonic producing load obtained from analysing the harmonic contribution changes. Based on voltage and current signature analysis, the distribution of harmonic components can be divided into three zones. To distinguish between the harmonic producing loads, the harmonic components are observed at these zones to get the signature recognition pattern. The result demonstrate that periodogram technique accurately diagnose and distinguish the type of harmonic sources in the distribution system
PWM-Switching pattern-based diagnosis scheme for single and multiple open-swi...ISA Interchange
This paper deals with a fault detection technique for insulated-gate bipolar transistors (IGBTs) open-circuit faults in voltage source inverter (VSI)-fed induction motor drives. The novelty of this idea consists in analyzing the pulse-width modulation (PWM) switching signals and the line-to-line voltage levels during the switching times, under both healthy and faulty operating conditions. The proposed method requires line-to-line voltage measurement, which provides information about switching states and is not affected by the load. The fault diagnosis scheme is achieved using simple hardware and can be included in the existing inverter system without any difficulty. In addition, it allows not only accurate single and multiple faults diagnosis but also minimization of the fault detection time to a maximum of one switching period (). Simulated and experimental results on a 3-kW squirrel-cage induction motor drive are displayed to validate the feasibility and the effectiveness of the proposed strategy.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IRJET - Study of Technical Parameters in Grid-Connected PV SystemIRJET Journal
This document studies the technical parameters of a grid-connected photovoltaic (PV) system. It models the various components of the PV system, including the PV array, maximum power point tracking, DC-DC converter, inverter, and inverter controller. It also models an islanding scenario where the PV system continues supplying local loads after disconnecting from the grid. Simulation results show that under islanding, the voltage and frequency waveforms vary depending on whether PV generation is less than, greater than, or equal to the local load. When PV power is greater or less than load power, voltage sags or swells and frequency increases linearly after islanding occurs. With equivalent PV and load power, voltage and frequency remain
Average dynamical frequency behaviour for multi-area islanded micro-grid netw...TELKOMNIKA JOURNAL
A micro-grid is a part of power system which able to operates in grid or islanding mode. The most important variable that able to give us information about the stability in islanded micro-grid network is the frequency dynamical responses. The frequency analysis for multi-area micro-grid network model may involve a complicated of mathematical equations. This makes the researcher intending to omit several unnecessary parameters in order to simplify the equations. The purpose of this paper is to show an approach to derive the mathematical equations to represent the average behavior of frequency dynamical responses for two different micro-grid areas. Both of networks are assumed to have non-identical distributed generator behavior with different parameters. The prime mover and speed governor systems are augmented with the general swing equation. The tie line model and the information of rotor angle was considered. Then, in the last section, the comparison between this technique with the conventional approach using centre of inertia (COI) technique was defined.
IRJET- Review: Different Techniques for ARC Flash and Fault Analysis and Clas...IRJET Journal
This document discusses several techniques for analyzing and classifying arc flashes and faults in direct current (DC) photovoltaic systems. It begins by introducing arc flashes as a safety hazard in large photovoltaic systems operating at high voltages and currents. It then describes several models and analysis methods for estimating the thermal energy released during arc flashes in DC photovoltaic systems. These include models based on circuit theory and existing DC arc flash models. The document also evaluates techniques such as wavelet transform analysis, support vector machines, entropy calculations, and hidden Markov models for detecting and classifying arc faults using voltage and current measurements. It concludes by presenting experimental validations of some of these arc fault detection methods on laboratory DC photovolta
Fpga versus dsp for wavelet transform based voltage sags detectionCecilio Martins
This document compares the use of field programmable gate arrays (FPGAs) and digital signal processors (DSPs) for fast detection of voltage sags in electrical power systems using a wavelet transform technique. It presents a system implemented using both an FPGA and DSP to detect artificially injected 500ms voltage sags in a laboratory power grid setup. The results show that both the FPGA and DSP implementations efficiently detected the sags in real-time, demonstrating their potential for power quality monitoring applications.
Open-Switch Fault-Tolerant Control of a Grid-Side Converter in a Wind Power G...IJPEDS-IAES
A fault-tolerant technique of a grid-side converter (GSC) is a very important
task because the unbalanced grid power endangers the overall system. Since
the GSC is very sensitive to grid disturbance, the complete system needs to
be stopped suddenly once an open-switch fault occurs. To improve the
reliability of system, the continuous operation should be guaranteed. In this
paper, a redundant topology based fault-tolerant algorithm is proposed for a
GSC in a wind power generation system. The proposed scheme consists of
the fault detection and fault-tolerant algorithms. The fault detection
algorithm employs the durations of positive and negaitive cycles of threephase
grid currents as well as normalized root mean square (RMS) currents.
Once a fault is detected, the corresponding faulty phase is identified and
isolated to enable the fault-tolerant operation. The faulty phase is replaced by
redundant one rapidly to recover the original shape of the grid currents,
which ensures the continuity in operation. In contrast with the conventional
methods, the proposed fault detection and fault-tolerant algorithms work
effectively even in the presence of the open faults in multiple switches in the
GSC. Simulation results verify the effectiveness of the proposed fault
diagnosis and fault-tolerant control algorithms.
Today power electronics play an important role in the electric industry. Power electronic converters are an inseparable component in power systems. One of these converters is DC/AC inverter that is widely used in power systems, industrial applications, electric motor drive and electric vehicles. Due to the tense situation with the complexity that exists in these applications, inverters are exposed to failure. The fault occurring in inverter can cause disturbance and damaging harmonics, cut some industrial processes to in the power system or in the case of electric vehicles, causing irreparable damage. For this reason, detecting faults in the inverter is very important. In this paper, open circuit fault of IGBT in an electric vehicle has been examined. We use three-phase current and wavelet transform to identify the state of the system and we can extract current waveform characteristics. We use neural network algorithm for fault detection and classification. An electric vehicle in 5 different speeds and 5 different torque and a total of 220 failure modes have been studied and tested. The results show the method has been succeeded to detection all forms of defined faults.
Review on Different Techniques for Differential Protection of Power TransformerIRJET Journal
This document reviews different techniques for differential protection of power transformers. It begins with an introduction to the importance of protecting expensive power transformers from internal faults. It then summarizes several existing techniques for digital relaying, including signal processing methods, model-based techniques, and artificial neural networks. The remainder of the document discusses specific techniques in more detail, including methods using current and voltage ratios, wavelet transforms, second central moment analysis, convolutional neural networks, and wavelet energy entropy. Each technique is evaluated based on its ability to quickly and reliably distinguish between internal faults and transient events like inrush currents. The reviewed methods generally demonstrate improved performance over traditional techniques in differentiating fault types and avoiding maloperation.
Evaluation of wind-solar hybrid power generation system based on Monte Carlo...IJECEIAES
The application of wind-photovoltaic complementary power generation systems is becoming more and more widespread, but its intermittent and fluctuating characteristics may have a certain impact on the system's reliability. To better evaluate the reliability of stand-alone power generation systems with wind and photovoltaic generators, a reliability assessment model for stand-alone power generation systems with wind and photovoltaic generators was developed based on the analysis of the impact of wind and photovoltaic generator outages and derating on reliability. A sequential Monte Carlo method was used to evaluate the impact of the wind turbine, photovoltaic (PV) turbine, wind/photovoltaic complementary system, the randomness of wind turbine/photovoltaic outage status and penetration rate on the reliability of Independent photovoltaic power generation system (IPPS) under the reliability test system (RBTS). The results show that this reliability assessment method can provide some reference for planning the actual IPP system with wind and complementary solar systems.
This document discusses a novel approach for detecting power islands in distribution networks using transient signals generated during islanding events. The proposed method uses pattern recognition techniques including discrete wavelet transform and decision tree classification. Wavelet transform is used to extract features from transient current and voltage signals. A decision tree classifier is then trained to categorize the transients as islanding or non-islanding events based on the extracted features. The technique is tested on a medium voltage distribution system and results indicate it can accurately detect islanding events very fast.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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This document summarizes a study that performed contingency analysis on Nigeria's 330kV power transmission network to identify vulnerabilities. Fast Decoupled Load Flow (FDLF) analysis was used to simulate the impact of single line outages. When the Kainji-GS line went down, several buses experienced low voltages and several lines saw power losses over 5%. The performance indices of each line were calculated and ranked based on their severity to identify the most critical lines.
This document summarizes a research paper that proposes a network-based coordination control technique for synchronizing a microgrid with the main electric power system during reconnection. A microgrid contains multiple distributed generators that must be controlled cooperatively to satisfy synchronization criteria. The proposed method uses a microgrid central controller that communicates with distributed generator controllers over a communication network. The controller calculates frequency, voltage, and phase offset commands to minimize differences between the microgrid and main grid. Simulation results using Matlab/Simulink show the technique can reliably synchronize the microgrid for network delays up to 200ms but not for delays of 500ms or greater.
Compared to a time-based maintenance schedule, condition-based
maintenance provides better diagnostic information on the health condition
of the different wind turbine components and subsystems. Rather than using
an offline condition monitoring technique, which require the WT to be taken
out of service, online condition monitoring does not require any interruption
on the WT operation. The online condition monitoring system uses different
types of sensors such as vibration, acoustic, temperature, current/voltage etc.
Using a machine learning approach, we aim to establish a data driven fault
prognosis framework. Instead of traditional wired communications, wireless
communication systems such as wireless sensor network have the advantages
of easier installation and lower capital cost. We propose the use of WSN for
collecting and transmitting the condition monitoring data to enhance the
reliability of wind parks. Using data driven approach the collective health of
the WP can be represented based on the condition of the individual wind
turbines, which can be used for predicting the remaining useful life of the
system.
Cetc11 - Wireless Magnetic Based Sensor System For Vehicles ClassificationNokia Networks
This paper presents a full pre-industrial prototype implementation of an innovative system capable to
detects vehicles and classify them. This work study the architecture of a full autonomous road sensor
network that count and classify passing vehicles, reducing the impact of conventional wired systems in
roads, by dramatically reducing their installation and operational costs. The power supply can be supported
either by solar harvest or by none rechargeable batteries. For communications the IEEE 802.15.4 with
ZigBee on top are used to transmit digitalized vehicle’s magnetic analogue signatures up to a vehicles
classification server. This information is acquired using a magnetometer translating in voltage the variation
of the natural magnetic field of the earth caused by ferromagnetic components of a vehicle passing over the
sensor. Results show clearly that it is possible to distinguish vehicles magnetic signatures per model and
class even at different speeds.
This document discusses research on measuring the sag of transmission lines using non-contact electric field sensing. It presents a study that uses finite element modeling to analyze the electric field distribution of conductors, establish relationships between the electric field and transmission line parameters, and propose a simplified sag calculation model. Simulation results assess the impact of transmission line parameters on sag measurements. Measurement schemes are designed for both single and dual-circuit transmission lines. The goal is to enable simple and efficient sag monitoring using electric field measurements from sensor arrays to help maintain power grid stability.
Transmission line is one the important compnent in protection of electric power system because the transmission line connects the power station with load centers.
The fault includes storms, lightning, snow, damage to insulation, short circuit fault [1].
Fault needs to be predicted earlier in order to be prevented before it occur
This document proposes using Particle Swarm Optimization (PSO) to detect faults on overhead transmission lines. It will involve simulating various fault types using PSCAD/EMTDC software, developing mathematical models of the voltage and current signals, and applying PSO in MATLAB to analyze the signals and detect faults. Preliminary results show simulation of five fault types. The developed method is expected to accurately detect faults before disturbances occur to maintain power system stability.
IRJET- Frequency-Based Energy Management in Islanded MicrogridIRJET Journal
1) The document discusses a frequency-based energy management system for an islanded microgrid.
2) It proposes using variations in grid frequency as a signal to coordinate energy production from renewable sources and energy storage as conditions change.
3) When battery is nearly full or solar production is high, frequency will rise and signal to curtail renewable production. When battery is low, frequency falls and loads can be shed.
A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Peri...IJECEIAES
This paper presents a diagnostic analytics of harmonic source signature recognition of rectifier and inverter-based load in the distribution system with single-point measurement at the point of common coupling by utilizing Periodogram. Signature recognition pattern is used to distinguish the harmonic sources accurately by obtaining the distribution of harmonic and interharmonic components and the harmonic contribution changes. This is achieved by using the significant signature recognition of harmonic producing load obtained from analysing the harmonic contribution changes. Based on voltage and current signature analysis, the distribution of harmonic components can be divided into three zones. To distinguish between the harmonic producing loads, the harmonic components are observed at these zones to get the signature recognition pattern. The result demonstrate that periodogram technique accurately diagnose and distinguish the type of harmonic sources in the distribution system
PWM-Switching pattern-based diagnosis scheme for single and multiple open-swi...ISA Interchange
This paper deals with a fault detection technique for insulated-gate bipolar transistors (IGBTs) open-circuit faults in voltage source inverter (VSI)-fed induction motor drives. The novelty of this idea consists in analyzing the pulse-width modulation (PWM) switching signals and the line-to-line voltage levels during the switching times, under both healthy and faulty operating conditions. The proposed method requires line-to-line voltage measurement, which provides information about switching states and is not affected by the load. The fault diagnosis scheme is achieved using simple hardware and can be included in the existing inverter system without any difficulty. In addition, it allows not only accurate single and multiple faults diagnosis but also minimization of the fault detection time to a maximum of one switching period (). Simulated and experimental results on a 3-kW squirrel-cage induction motor drive are displayed to validate the feasibility and the effectiveness of the proposed strategy.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IRJET - Study of Technical Parameters in Grid-Connected PV SystemIRJET Journal
This document studies the technical parameters of a grid-connected photovoltaic (PV) system. It models the various components of the PV system, including the PV array, maximum power point tracking, DC-DC converter, inverter, and inverter controller. It also models an islanding scenario where the PV system continues supplying local loads after disconnecting from the grid. Simulation results show that under islanding, the voltage and frequency waveforms vary depending on whether PV generation is less than, greater than, or equal to the local load. When PV power is greater or less than load power, voltage sags or swells and frequency increases linearly after islanding occurs. With equivalent PV and load power, voltage and frequency remain
Average dynamical frequency behaviour for multi-area islanded micro-grid netw...TELKOMNIKA JOURNAL
A micro-grid is a part of power system which able to operates in grid or islanding mode. The most important variable that able to give us information about the stability in islanded micro-grid network is the frequency dynamical responses. The frequency analysis for multi-area micro-grid network model may involve a complicated of mathematical equations. This makes the researcher intending to omit several unnecessary parameters in order to simplify the equations. The purpose of this paper is to show an approach to derive the mathematical equations to represent the average behavior of frequency dynamical responses for two different micro-grid areas. Both of networks are assumed to have non-identical distributed generator behavior with different parameters. The prime mover and speed governor systems are augmented with the general swing equation. The tie line model and the information of rotor angle was considered. Then, in the last section, the comparison between this technique with the conventional approach using centre of inertia (COI) technique was defined.
IRJET- Review: Different Techniques for ARC Flash and Fault Analysis and Clas...IRJET Journal
This document discusses several techniques for analyzing and classifying arc flashes and faults in direct current (DC) photovoltaic systems. It begins by introducing arc flashes as a safety hazard in large photovoltaic systems operating at high voltages and currents. It then describes several models and analysis methods for estimating the thermal energy released during arc flashes in DC photovoltaic systems. These include models based on circuit theory and existing DC arc flash models. The document also evaluates techniques such as wavelet transform analysis, support vector machines, entropy calculations, and hidden Markov models for detecting and classifying arc faults using voltage and current measurements. It concludes by presenting experimental validations of some of these arc fault detection methods on laboratory DC photovolta
Fpga versus dsp for wavelet transform based voltage sags detectionCecilio Martins
This document compares the use of field programmable gate arrays (FPGAs) and digital signal processors (DSPs) for fast detection of voltage sags in electrical power systems using a wavelet transform technique. It presents a system implemented using both an FPGA and DSP to detect artificially injected 500ms voltage sags in a laboratory power grid setup. The results show that both the FPGA and DSP implementations efficiently detected the sags in real-time, demonstrating their potential for power quality monitoring applications.
Open-Switch Fault-Tolerant Control of a Grid-Side Converter in a Wind Power G...IJPEDS-IAES
A fault-tolerant technique of a grid-side converter (GSC) is a very important
task because the unbalanced grid power endangers the overall system. Since
the GSC is very sensitive to grid disturbance, the complete system needs to
be stopped suddenly once an open-switch fault occurs. To improve the
reliability of system, the continuous operation should be guaranteed. In this
paper, a redundant topology based fault-tolerant algorithm is proposed for a
GSC in a wind power generation system. The proposed scheme consists of
the fault detection and fault-tolerant algorithms. The fault detection
algorithm employs the durations of positive and negaitive cycles of threephase
grid currents as well as normalized root mean square (RMS) currents.
Once a fault is detected, the corresponding faulty phase is identified and
isolated to enable the fault-tolerant operation. The faulty phase is replaced by
redundant one rapidly to recover the original shape of the grid currents,
which ensures the continuity in operation. In contrast with the conventional
methods, the proposed fault detection and fault-tolerant algorithms work
effectively even in the presence of the open faults in multiple switches in the
GSC. Simulation results verify the effectiveness of the proposed fault
diagnosis and fault-tolerant control algorithms.
Today power electronics play an important role in the electric industry. Power electronic converters are an inseparable component in power systems. One of these converters is DC/AC inverter that is widely used in power systems, industrial applications, electric motor drive and electric vehicles. Due to the tense situation with the complexity that exists in these applications, inverters are exposed to failure. The fault occurring in inverter can cause disturbance and damaging harmonics, cut some industrial processes to in the power system or in the case of electric vehicles, causing irreparable damage. For this reason, detecting faults in the inverter is very important. In this paper, open circuit fault of IGBT in an electric vehicle has been examined. We use three-phase current and wavelet transform to identify the state of the system and we can extract current waveform characteristics. We use neural network algorithm for fault detection and classification. An electric vehicle in 5 different speeds and 5 different torque and a total of 220 failure modes have been studied and tested. The results show the method has been succeeded to detection all forms of defined faults.
Review on Different Techniques for Differential Protection of Power TransformerIRJET Journal
This document reviews different techniques for differential protection of power transformers. It begins with an introduction to the importance of protecting expensive power transformers from internal faults. It then summarizes several existing techniques for digital relaying, including signal processing methods, model-based techniques, and artificial neural networks. The remainder of the document discusses specific techniques in more detail, including methods using current and voltage ratios, wavelet transforms, second central moment analysis, convolutional neural networks, and wavelet energy entropy. Each technique is evaluated based on its ability to quickly and reliably distinguish between internal faults and transient events like inrush currents. The reviewed methods generally demonstrate improved performance over traditional techniques in differentiating fault types and avoiding maloperation.
Evaluation of wind-solar hybrid power generation system based on Monte Carlo...IJECEIAES
The application of wind-photovoltaic complementary power generation systems is becoming more and more widespread, but its intermittent and fluctuating characteristics may have a certain impact on the system's reliability. To better evaluate the reliability of stand-alone power generation systems with wind and photovoltaic generators, a reliability assessment model for stand-alone power generation systems with wind and photovoltaic generators was developed based on the analysis of the impact of wind and photovoltaic generator outages and derating on reliability. A sequential Monte Carlo method was used to evaluate the impact of the wind turbine, photovoltaic (PV) turbine, wind/photovoltaic complementary system, the randomness of wind turbine/photovoltaic outage status and penetration rate on the reliability of Independent photovoltaic power generation system (IPPS) under the reliability test system (RBTS). The results show that this reliability assessment method can provide some reference for planning the actual IPP system with wind and complementary solar systems.
This document discusses a novel approach for detecting power islands in distribution networks using transient signals generated during islanding events. The proposed method uses pattern recognition techniques including discrete wavelet transform and decision tree classification. Wavelet transform is used to extract features from transient current and voltage signals. A decision tree classifier is then trained to categorize the transients as islanding or non-islanding events based on the extracted features. The technique is tested on a medium voltage distribution system and results indicate it can accurately detect islanding events very fast.
Similar to A comprehensive fuzzy-based scheme for online detection of operational and topological changes (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
A comprehensive fuzzy-based scheme for online detection of operational and topological changes
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 4, August 2022, pp. 3396~3409
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i4.pp3396-3409 3396
Journal homepage: http://ijece.iaescore.com
A comprehensive fuzzy-based scheme for online detection of
operational and topological changes
Amin Damanjani, Mohamad Hosseini Abardeh, Azita Azarfar, Mehrdad Hojjat
Department of Electrical and Computer Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran
Article Info ABSTRACT
Article history:
Received Dec 9, 2020
Revised Mar 10, 2022
Accepted Mar 23, 2022
Operational modes and topological changes affect power flow in the power
systems. As a result, a broad spectrum of protection issues may happen in
the power system. So, both the operational and topological changes should
be detected fast to prevent blackouts. On the other hand, the existing
detection schemes are complex in analyzing and implementation. Therefore,
there is a need for an online scheme to identify the network's topology and
operation mode simultaneously without complex computations and
additional communication infrastructures. To this end, a comprehensive
scheme is proposed in which the changes are detected by analyzing the
power flow obtained from the network. For this purpose, line outage
contingencies and operation modes are defined in rules to be used in a fuzzy
inference system (FIS) as a decision-making tool. The proposed scheme can
be implemented on existing lines as a communication infrastructure and
determines the network’s status in an online manner. Also, in comparison to
the existing schemes, the proposed scheme reduces the complexity and the
computational burden. The proposed scheme is implemented on IEEE 8-bus
system and the results proved its efficiency.
Keywords:
Contingency
Fuzzy inference system
Line outage
Operation mode
Power system protection
This is an open access article under the CC BY-SA license.
Corresponding Author:
Mohamad Hosseini Abardeh
Department of Electrical and Computer Engineering, Shahrood Branch, Islamic Azad University
Shahrood, Iran
Email: mohamad.hosseini@mail.um.ac.ir
1. INTRODUCTION
Transmission lines play a vital role in the power system, as they transfer electric power from power
plants to the users [1]. Planned events such as maintenance operations or unplanned events, such as short
circuit faults, which are the most common type of faults in the power system, can lead to line outage
contingencies. Moreover, the single-line outage, if not detected and treated quickly, may result in multiple
line outages in a few minutes and eventually lead to a costly blackout in less than one hour [2]. On the other
hand, the protection system would encounter some problems due to the acceleration of the microgrid
technology development in distribution networks and changes in the amount and direction of power flow
levels at different modes of operation (i.e., grid-connected and islanded) [3], [4]. When a microgrid is
operating without a connection to the main network, it is said to be operating in islanded mode [5]. When the
network is in grid-connected mode, an external grid is connected to the main grid to feed the loads. Thus, the
power flow value obtained by the measurements devices in islanded mode will be much smaller in
comparison with the grid-connected mode.
The operational and topological changes in the networks will encounter the protection system to
some protection problems due to variations in the level of the power. So, applying a scheme to detect the
network’s status both operational and topological for subsequent adaptive actions and preventing protection
2. Int J Elec & Comp Eng ISSN: 2088-8708
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problems is mandatory. There have been lots of methods to detect the networks’ operation mode known as
island detection methods. These methods utilized non-phasor measurements of network parameters and
characteristics analyses [6]–[15], or phasor measurements data obtained from phasor measurement units
(PMUs) [16]–[18].
In literature [6], [7] the wavelet transform is utilized for island detection. Wavelet transform and
S-transform are applied on features that are extracted from the negative sequence component of the voltage
signal for islanding detection [6]. Analysis of the transient signals of voltage generated during
non-islanding events is used for islanding detection [7]. Extraction of signal energies in different levels of
discrete wavelet transform is utilized as features and used in a decision tree as a classifier to identify the
islanding condition.
Marchesan et al. [8] utilized frequency oscillation estimation to distinguish the islanding from other
events in distribution systems. To this end, a small window of data is used to estimate the oscillation of
frequency, which leads to a significant reduction in islanding detection time. Voltage unbalances and total
harmonic distortion detection along with reactive power variation are utilized for island detection in [9]. The
reactive power variation is only used when the islanding condition is detected by voltage unbalance and total
harmonic distortion that leads to improvement in islanding detection performance and resolved power quality
problems. Also, in other research, voltage unbalance and voltage total harmonic distortion from all phases are
used for islanding detection [10].
Chen et al. [11] utilized the correlation factor between the reactive power disturbance and frequency
variation along with three criteria including voltage variation, voltage unbalance, and rate of change of
frequency to improve the detection time for island detection. Also, in study [12] an islanding detection
algorithm based on reactive power control is proposed that provides the reactive power reference for the
distributed generation (DG). This helps the system to keep the consistency of the frequency variation due to
active and reactive power mismatches and consequently accelerate the islanding detection speed.
Moreover, in study [13] an islanding detection technique is proposed that is based on the derivation
of components of the voltage signals as islanding detection factor. The island detection factor is compared
with the preset threshold that is the steady-state value of voltage during a normal situation (non-islanding). If
this value is greater than the preset threshold, the network is in islanding condition.
Guo et al. [14] proposed a component analysis method for islanding detection that improves the
traditional rate of change of frequency technique that is not reliable for the time-varying process in fault and
noisy situations. The technique obtains data from PMUs for system monitoring. To this end, a simple control
procedure is used to monitor the transients. Therefore, by offline modeling and online monitoring, the
islanding situation is detected. As a recent non-phasor based method, the islanding mode is detected using the
rate of change of power technique based on the terminal voltage of the DG [15].
Also, lots of research have been introduced for island detection that are based on phasor
measurements. In study [16] frequency difference and the change of voltage angle difference are utilized for
island detection. Data are collected by the frequency disturbance recorder that is a single-phase PMU. To this
end, the sensitivity analysis is performed on the thresholds of the frequency deviation and angle deviation,
and the insensitive interval of the two mentioned thresholds is obtained to set the final threshold for decision
making of islanding or non-islanding.
Recently, in studies [17], [18] some phasor-based methods have been provided to detect the islanded
mode. the method employed the angle difference between positive and negative sequence phase angle of the
voltage signal and angle difference under a normal scenario to detect the islanded conditions [17]. Also, in
study [18] a predictive model is used for islanding detection based on measuring phasors obtained from the
real-time system. The measurements are used as inputs of an artificial neural network for initiating the model
that is designed based on the occurrence of islanding in terms of probability to estimate the probability of the
islanding.
On the other hand, there are methods to detect the networks’ topological changes known as line
outage detection methods including non-phasor measurements of network parameters and characteristics
analyses [19], and phasor measurements data [20]-[34]. In study [19] a line outage detection algorithm is
proposed that used a divide-based procedure. First, the distribution network is divided into sub-networks,
which communicate with their neighbors with separate control areas. Then a load estimation procedure is
applied across the control areas of each sub-network to detect the outage independently by voltage magnitude
measurements.
Direct current (DC) and alternating current (AC) models of power flow are utilized to estimate the
locations of lines that are under outage [20]. Line outages are modeled using power flow equations by the
magnitude and angle of voltage that are obtained by PMU. Also, the losses corresponding to the post-outage
of lines are modeled and used in the power flow equations. Finally, a regularized least square method is used
to solve the model and to estimate the locations of lines that are under outage.
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Babakmehr et al. [21] considered the power network as a single graph and initialized the
mathematical formulation of the power line outage detection problem using the DC power flow model. Then,
a sparse representation-based formulation is applied for this problem. The data to be used in the sparse outage
vector are obtained by PMUs. Based on the sparse vector representation the line outage is detected.
Alam et al. [22] proposed an algorithm for the detection of line outage that is based on a comparison
of the current phasor measurements. To this end, the phasors of current that are obtained from power flow
simulation for some line outage contingencies are stored in matrix form. When an actual outage happened,
the real-time data of current phasors that are obtained from PMU are compared with the stored values for
detection purposes. By utilizing the virtual adaptive observers that applied on voltage phasor angle obtained
from the PMUs, the connectivity status between buses is monitored to detect the line outage contingency
[23]. The power network is modeled by the weighted Laplacian matrix and the lines’ admittance value could
be estimated. If the admittance values decayed to zero, the line is in outage status.
As the other study, a scheme is proposed in [24] to detect line outages based on changes in
impedance matrix due to voltage phasor variations in outage conditions. So, calculation of changes in
impedance matrix due to each single line outage is performed in an offline manner, stored, and compared
with online data for outage detection. In study [25] a methodology is proposed that is based on the state
estimation. This method utilized a sparse vector to model the network using the voltage angle values
corresponding to pre-outage and post-outage to find the variations in network topology and to estimate the
power system status. Also, in study [26] an algorithm has been developed for the online detection of line
outage contingencies. The proposed algorithm is based on a parameter namely line current distribution factor.
This factor is the ratio of changes in current due to fault to the base case current flow through the line that is
stored as a matrix in an offline manner. In the proposed algorithm, the current passing through the line is
monitored by PMU and compared with the line current distribution factor for detection purposes. Moreover
in study [27], an algorithm is proposed that utilized an estimation of the pre-outage power flow of lines for
detection purposes. Line outage can be detected based on the phasor angle difference at the observable buses
with respect to the pre-outage angles.
Several studies [28]–[31] solved the line outage problem by some probability-based methods. In
study [28] a scheme is proposed that is based on the variations of the angles between the voltage of the bus
and the injected active power. To this end, the statistical models from pre and post-outage of the angle
variations are extracted. Then, the scheme evaluates the probability of a normal condition against the
abnormal condition continuously. Under normal conditions, system outputs follow a common distribution of
probability. When an outage occurred, the density of the probability function changes. The line is detected in
outage condition when the statistics cross the pre-defined threshold. In other words, line outage can be
detected by continuous online monitoring and comparing the probability of every outage scenario with the
offline pre-defined threshold value. Also, a method is proposed that utilized the linearized model of the AC
power flow with current and voltage phasor data to detect the most probable location of line outages [29]. In
this method, a comparison procedure between the variations in observed and calculated values in the phase
angles of voltage and active power flowing through the lines is performed to detect the locations of lines
under outages. Moreover, the changes in network power injection at each bus are modeled by the probability
distribution of generation and demand [30]. Then, the model is used to relate the distribution's probability of
the injected power and the voltage angles through a mapping procedure. Finally, a decision-maker is used to
observe the sequence of changes in bus voltage phase angle in the condition of change due to a line outage.
In addition, a probability-based method is proposed that is based on a stochastic search method to estimate
the probability of line outage occurrence [31]. To this end, the voltage phasor angle at each bus is measured
and sent to the control center for detection purposes.
The dynamics of the power system are utilized to model the system by voltage phase angle
measurements [32]. The model is applied in the dynamic CuSum test that is used to obtain the behavior of the
system in transient situations. If any of the test statistics crosses a pre-determined threshold, then the
corresponded line is considered under outage.
Several studies [33], [34] presented state-of-the-art approaches in line outage detection. Machine
learning is used to locate line outages in [33]. To this end, a feature extraction method is used to obtain the
dynamic characteristics of voltage phase angles when the topology changes. Also, in study [34] a
methodology is proposed that is based on the phasors of current. In this algorithm, the currents of buses for
various line outage cases are obtained from the simulations and are stored. In the case of an actual outage, the
currents of buses from PMUs are compared with the previously stored values for line outage detection.
In the above mentioned studies the complexity, high computational burden and not being
cost-effective can be considered as the research gap. In other words, both existing operational and topological
detection methods suffer from complex detection algorithms to analyze, heavy mathematical computations
especially for large-scale networks, or expensive hardware such as PMU that must be installed in the
4. Int J Elec & Comp Eng ISSN: 2088-8708
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network. More importantly, the lack of a comprehensive detection scheme to cover the detection of both the
operation modes and line outage contingencies leads to a need for a less complex, less computational burden
and cost-effective detection scheme to detect both the operation modes and lines' status simultaneously. For
this purpose, this study provides a comprehensive and practical scheme that ensures the detection of both
operating modes (grid-connected or islanded) and the topological changes (line outage contingencies). The
detection of operational and topological changes is performed utilizing data that are obtained in an online
manner. Applying the proposed scheme, any changes in the network’s operation mode and single-line outage
contingencies are detected in the control center utilizing a fuzzy inference system (FIS) as an artificial
intelligence (AI) tool to overcome the complexity and high computational burden of existing methods based
on the current and voltage data obtained from the network without the need to any additional measurement
devices. The transformation of the network’s data is performed under existing power lines, and consequently,
the status of the network can be determined cost-effectively.
In this paper, first, the details of the proposed detection scheme are explained. Then the detection
procedure of the network’s operation mode and topology which are all designed as a computer program is
explained. Finally, the effectiveness of the proposed scheme for the detection of the network’s operation
mode and single-line outage contingencies is evaluated. To summarize, the main contribution of this paper is
introducing a cost-effective, less complex, and less computational burden intelligent scheme to detect both
modes of operations and network topology in an online manner simultaneously.
The paper is structured: the explanations of the proposed detection scheme are provided in section 2.
The FIS approach for the detection procedure is addressed in section 3. The results of implementing the
proposed detection scheme on a sample network are illustrated in section 4. Finally, the conclusion is
remarked in section 5.
2. PROPOSED DETECTION SCHEME
For protection purposes, there is a need for a comprehensive scheme with the capability of detecting
both modes of operation and lines’ status in an online manner. This paper proposes a scheme based on an AI
tool for detecting the single-line outage contingencies and the network mode of operation. The AI tool
applied in the proposed detection scheme is a FIS which determines the operational and topological status of
the network as the pre-defined scenarios. The data used for the FIS is independent of the extra
communication devices and relies on existing power lines as communication infrastructure for receiving
input data from the network for the detection procedure.
In the proposed scheme, local data are measured by current transformers (CTs) and potential
transformers (PTs) and sent to the control center. In the control center, the obtained data are analyzed to
determine the final status of the network’s scenario. The proposed scheme concentrated on AI for analyzing
data to determine the mode of operation and topology simultaneously. Figure 1 provides the proposed
scheme’s procedure in detail.
According to Figure 1, the scheme is designed in a way that performs in an online manner. The
signals from relays are obtained and divided into two separate detection procedures. The first procedure
transforms the analog signals to digital signals utilizing the relays to find the power flow passing through the
lines' circuit breakers (CBs) and make the decision if any line is in outage condition or not. In the second
procedure, the analog data are utilized to obtain the total power flowing through the lines. This value
determines the operation mode of the network. Finally, the results of both procedures are used as inputs of a
FIS as a decision-making tool to detect the operational and topological changes of the network and determine
the final status of the network’s scenario.
Figure 1. The proposed detection scheme
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3. FUZZY APPROACH FOR DETECTION PROCEDURE
FIS has been widely applied for making the decision [35]. The key advantage of FIS is complete
consistency and always giving the same outputs when receives the same inputs [36]. In this paper, a scheme
based on FIS is proposed for the detection procedure of the network's operation mode and single-line outage
contingencies. A typical FIS consists of three components including fuzzification, fuzzy inference engine,
and defuzzification as shown in Figure 2 [37]. In FIS, numerical input variables go through the fuzzification
block and are converted to linguistic variables to act as the input of the inference engine. These fuzzy inputs
are converted to fuzzy outputs utilizing the fuzzy inference engine by defining the rules. Finally, in the
defuzzification block, they are converted to numerical values as the output of the system. A fuzzy set A in
X={x1, x2, …, xn} as the universe of discourse is defined [38],
𝐴 = {(𝑥. 𝜇𝐴(𝑥))} 𝑥 ∈ 𝑋 (1)
where µA(x) is the grade of membership x in A and µA: X → [0,1], is called the membership function (MF)
[38]. Therefore, inputs and output are defined as MFs in FIS. A FIS is made of a collection of several rules of
the type IF-THEN [39],
If x1 is 𝐴 and x2 is 𝐵. then 𝑌 is 𝐶 (2)
where x1 and x2 are inputs and C is the output [39]. This type of rule is utilized for detection purposes in the
proposed detection scheme. Therefore, x1 and x2 can be defined as two inputs of the proposed scheme
corresponded to the operation mode and line's status. Also, C can be defined as the goal output which is the
scenario index. The application of FIS for the detection of operational and topological changes is explained.
Figure 2. Functional blocks of a FIS
3.1. FIS for operation mode detection
The real-time data corresponded to currents and voltages obtained from the CTs and PTs are
aggregated as analog signals using a relay to be used for operation mode detection. As aforementioned, some
networks are capable of working at two different operation modes (i.e., grid-connected and islanded). In
islanded mode, the network is working by DGs located at different busses to feed the loads. When the
operation mode is changed to grid-connected, the network is connected to an external grid to retain the
capability of feeding the loads. In such a situation, the power flowing through the network increases
significantly. By knowing the total power level passing through the lines, the operation mode can be
detected. So, this parameter, which is obtained as the analog data, is used as one of the inputs of the FIS in
the proposed detection scheme.
3.2. FIS for line status detection
In the proposed detection scheme, there is a need for digital data as input of the FIS to detect the
status of the lines. For this purpose, data are obtained from the CTs and PTs located at each end of the lines
as analog signals and converted to digital signals in the relay. To utilize the digital signals for the detection of
the lines’ status, the CBs’ status of the lines (open or closed) must be defined as binary values. For this
purpose, first, the active power flowing through a CB is defined using a DC approximation as (3) [40]:
𝐹𝑖𝑗 = 𝐵𝜀(𝜃𝑖 − 𝜃𝑗) (3)
where Fij indicates the active power flowing through each CB of the line between bus i and j with the phase
angle of θi and θj. Also, Bε indicates the susceptance of the line. Then, a binary value namely Uij is defined
for the status of each CB located at the line, as (4) [40]:
𝐹𝑖𝑗 = 𝑈𝑖𝑗𝐵𝜀(𝜃𝑖 − 𝜃𝑗) (4)
6. Int J Elec & Comp Eng ISSN: 2088-8708
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As can be interfered from (4), when the value of Uij is “1” the CB is in the closed condition and the
power flows through the line (normal status of the line), whereas “0” value indicates the CB of the line is in
the open condition and there is no power flowing through the line (line outage contingency). For the sake of
clarity, a scheme diagram of the line’s CBs, which receives the data at both ends of the corresponding buses
and pre-processes them in the control center for the line’s status detection procedure, is shown in Figure 3.
As it can be seen from Figure 3, the power flowing through the line’s CBs is received by the relays. Then
data are sent to the control center to be used by a FIS as a decision-making tool to determine the line’s status.
To consider the two CBs located at each end of the line and determine the final status of the binary variable
Uij to use as the other input of the FIS, a logic gate is used as shown in Figure 3.
Also, communication infrastructure is necessary to transform data from the network to the control
center for analyzing and decision–making procedures. Power line carrier communication (PLCC) is a widely
used telecommunication technology that uses the existing electrical transmission line as a medium to transmit
data [41]. Power transformation along with communication are the two tasks of PLCC. A modem is
responsible to transmits and receives data through the transmission line [42]. Utilizing the PLCC, data is sent
by the modulator and at the receiver’s side, a demodulator is responsible to retrieve data [42]. The frequency
shift technique is utilized for keying the binary data into a carrier signal and coupling it onto the power line
by the PLC modem (PLM). Finally, the other PLM at the receiver’s side will detect the signal and convert it
to binary data [42].
Figure 3. The schematic diagram for the procedure of the line’s status detection
4. IMPLEMENTATION OF THE PROPOSED DETECTION SCHEME
4.1. Network under study
The IEEE 8-bus system that is shown in Figure 4 is utilized to implement and evaluate the proposed
detection scheme [43]. The network operates at 300 MVA rates. Moreover, the network can be operated as
the grid-connected mode by connecting to the external grid with a capacity of 400 MVA. Also, a wind energy
farm (WEF) can be connected to bus 5 that includes four wind turbine generators (WTGs) of 2.5 MVA which
makes the capacity of the total power of WEF to 10 MVA rates [43]. The IEEE 8-bus system parameters are
available in [44].
4.2. Design and application of FIS
As above mentioned, in this study, FIS is the selected AI tool to be used for detection purposes. To
this end, in the proposed scheme, a FIS structure is designed and used for the detection of both the networks’
modes of operation and the single-line outage contingencies. Consequently, the network’s status can be
determined as the output of the FIS. There are two types of FIS namely Mamdani and Takagi-Sugeno (TS).
The TS type is similar to the Mamdani type, and the difference is that the output is a crisp function of the
input variables rather than a fuzzy proposition that appears in the Mamdani type [45]. So, the TS type is
chosen for the fuzzy part of the proposed detection scheme.
The inputs which are used in the FIS for detection purposes, are “LBK-ST” and “TOTAL-POWER”
which are corresponded to the lines' CBs' status and the network’s total power, respectively. Also, the output
is “SCE-INDEX” which is corresponded to the pre-defined indices of the network scenarios including
operational and topological changes. Since the simple implementation and fast computations, the triangular
MFs are used for the inputs of the FIS.
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Figure 4. IEEE 8-bus system
For this purpose, the “LBK-ST” as one of the inputs is defined by MFs “low” and “high” with the
range of (−1, 0, 1) and (0, 1, 2), respectively. MF “low” means the line is in outage condition and MF “high”
indicates the normal status of the line. The “TOTAL-POWER” as the second input receives the MFs namely
“low”, “medium” and “high” with the range of (0,300), (300,310), and (310,710) respectively. These MFs
correspond to islanded mode, islanded mode with WEF, and grid-connected mode, respectively. The
associated triangular MFs for the three pre-defined operation modes are defined:
𝜇𝑙𝑜𝑤(𝑥) =
{
0 𝑥 ≤ 0
𝑥
150
0 < 𝑥 ≤ 150
300−𝑥
300−150
150 < 𝑥 < 300
0 𝑥 ≥ 300
(5)
𝜇𝑚𝑒𝑑𝑖𝑢𝑚(𝑥) =
{
0 𝑥 ≤ 300
𝑥−300
305−300
300 < 𝑥 ≤ 305
310−𝑥
310−305
305 < 𝑥 < 310
0 𝑥 ≥ 310
(6)
𝜇ℎ𝑖𝑔ℎ(𝑥) =
{
0 𝑥 ≤ 310
𝑥−310
510−310
310 < 𝑥 ≤ 510
710−𝑥
710−510
510 < 𝑥 < 710
0 𝑥 ≥ 710
(7)
Also, the MFs correspond to the status of the lines’ CBs are defined:
𝜇𝑙𝑜𝑤(𝑥) = {
0 𝑥 ≤ −1
𝑥 + 1 − 1 < 𝑥 ≤ 0
1 − 𝑥 0 < 𝑥 < 1
0 𝑥 ≥ 1
(8)
𝜇ℎ𝑖𝑔ℎ(𝑥) = {
0 𝑥 ≤ 0
𝑥 0 < 𝑥 ≤ 1
2 − 𝑥 1 < 𝑥 < 2
0 𝑥 ≥ 2
(9)
R2
B 1
T1
B 7
G: Generator
T: Transformer
WEF: Wind Energy Farm
B: Bus
R: Relay
L: Load
Line 1-3 Line 3-4
G1
Line 1-6
Line 2-6
B 3 B 4
B 5
WEF
T2
B 6
B 8
R9 R3 R10
R4
R11
R5
R12
R7
R13
B 2
R8
R1
Line 5-6
Line
1-2
Line
4-5
G2
R14
L2
L3 L4
L5
External grid
R6
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A comprehensive fuzzy-based scheme for online detection of operational … (Amin Damanjani)
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According to the MFs defined for the CBs’ status and the power flow level in (5)-(9), the graphs of
MFs for inputs of the FIS are shown in Figure 5. Figure 5(a) indicates the MFs of power flow level. In
addition, Figure 5(b) shows the MFs corresponding to the CBs’ status.
(a)
(b)
Figure 5. Graph of MFs (a) power flow level and (b) CBs’ status
Based on the status of these MFs, the rules in the FIS for the proposed detection scheme are
formulated for the IEEE 8-bus system as shown in Table 1. It is to be noted that the linguistic terms L, M,
and H which are used in Table 1 are corresponded to “low”, “medium”, and “high” MFs, respectively. As can
be seen in Table 1, each scenario corresponds to the one single-line outage contingency in a specific network
operation mode. A fuzzy logic designer is used to design and test the FIS for modeling the system behaviors
under operational and topological changes. The designer in Figure 6, shows the status of lines’ CBs and the
total power as inputs and the index of the pre-defined scenario as the output.
Table 1. Fuzzy rules for IEEE 8-bus system
SCE-
INDEX
LBK-ST
(Line 1-2)
LBK-ST
(Line 1-3)
LBK-ST
(Line 3-4)
LBK-ST
(Line 4-5)
LBK-ST
(Line 5-6)
LBK-ST
(Line 2-6)
LBK-ST
(Line 1-6)
TOTAL-
POWER
1 L H H H H H H L
2 H L H H H H H L
3 H H L H H H H L
4 H H H L H H H L
5 H H H H L H H L
6 H H H H H L H L
7 H H H H H H L L
8 L H H H H H H M
9 H L H H H H H M
10 H H L H H H H M
11 H H H L H H H M
12 H H H H L H H M
13 H H H H H L H M
14 H H H H H H L M
15 L H H H H H H H
16 H L H H H H H H
17 H H L H H H H H
18 H H H L H H H H
19 H H H H L H H H
20 H H H H H L H H
21 H H H H H H L H
μ (X)
low
1
medium high
0 300 310 710 X
μ (X)
low
1
high
0
-1 X
1 2
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Figure 6 shows that the output obtained after defuzzification is in terms of scenario index. These are
numerical values vary from 1 to 21 corresponding to SCE-INDEX1 to SCE-INDEX21. Each scenario
corresponds to a single-line outage contingency in a pre-defined operation mode. So, by knowing the status
of the lines and the value of total power flowing through the network, the topological and operational status
of the network can be detected. The selection of the network’s scenario index is performed by fuzzy logic
toolbox using the graphical user interface (GUI) in MATLAB software and the results are shown in Figure 7.
In this Figure, the SCE-INDEX2, SCE-INDEX9, and SCE-INDEX16 are selected when line 1-3 is under
outage and the network is in islanded mode, islanded mode with WEF and grid-connected mode,
respectively.
Figure 6. Fuzzy logic designer for detection of operational and topological changes
In Figures 7(a), 7(b), and 7(c), the first eight columns represent the inputs, and the last column
represents the output. Inputs are the status of the lines’ CBs and the value of the total power of the network.
Also, the output is the related scenario index. As it is shown in Figures 7(a), 7(b), and 7(c), a text block of
FIS with 21 scenarios including single-line outage contingencies at the three pre-defined operation modes
(i.e., islanded mode, islanded mode with WEF, and grid-connected mode) is used for assessment of the FIS in
the proposed detection scheme. It is shown in Figure 7(a), that if the CBs of line 1-3 be open and 122 MVA
electric power flowing through the network, the FIS output indicates the scenario index 2 that is defined for
line 1-3 outage at islanded mode as per Table 1. Also, the selection of the scenario indices 9 and 16 are
shown in Figures 7(b), and 7(c) respectively. In Figure 7(b), scenario index 9 is selected when the network is
in islanded mode along with the connection of WEF while line 1-3 is under outage. At this condition, the
power flowing through the network is in the range of [300,310]. So, by the selection of 304 as the network
power when the CBs of line 1-3 are open (line 1-3 is in outage condition), index 9 is selected by the FIS as
the output. Moreover, when the loads exceed and the network is unable to feed the loads, the network is
connected to the external grid. At this condition, the power flowing through the network is raised to the range
of [310,710]. Figure 7(c) corresponds to this situation. As can be seen from this figure, the FIS output shows
scenario index 16 when the network power is 542 MVA and line 1-3 is in outage condition. To clarify, the
performance of the FIS for detection purposes is presented in Table 2. So, according to the performance of
the FIS, the network's status both operational and topological can be determined accurately.
10. Int J Elec & Comp Eng ISSN: 2088-8708
A comprehensive fuzzy-based scheme for online detection of operational … (Amin Damanjani)
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(a)
(b)
(c)
Figure 7. Network status selection in IEEE 8-bus system (a) scenario 2, (b) scenario 9, and (c) scenario 16
11. ISSN: 2088-8708
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Table 2. Performance of the FIS
Operational mode Total power (MVA) Number of outage line
1-2 1-3 3-4 4-5 5-6 2-6 1-6
Islanded mode 0-300 1 2 3 4 5 6 7 Scenario index
Islanded mode with WEF 300-310 8 9 10 11 12 13 14
Grid-connected mode 310-710 15 16 17 18 19 20 21
4.3. Method comparison
Generally, the detection methods in the power system can be divided into operational and
topological methods. In both categories, some methods are based on non-phasor measurements and analysis
of the network’s parameters such as current and voltage, and some methods are based on analysis of phasor
measurements including the angle of networks’ parameters. The non-phasor measurement-based methods
suffer from a high computational burden for final decision-making. On the other hand, the phasor
measurement-based methods suffer from complexity and expensive implementation due to the utilization of
PMUs.
To illustrate the superiority of the proposed method to the existing detection methods there is a
comparison given in Table 3. According to the comparison characteristics that are listed in Table 3, less
computational burden and reduction in complexity to analyze the networks’ parameters that are obtained
from existing measurement devices in the network utilizing the FIS for the detection procedure are the two
significant characteristics that the proposed method benefits from them. Also, the proposed method utilizes
the advantage of certainty of FIS since gives the same output by receiving the same inputs according to the
pre-defined fuzzy-based rules.
Moreover, the advantage of the PLCC technology is the capability to use existing power lines for
communication and data can be sent without any additional wiring or Ethernet-based infrastructures or PMUs
that are expensive in implementation. So, regardless of the probable defects on transferring data such as noise
that may mix with the communication signal and can be solved easily using filters or isolation circuits, no
need for additional communication infrastructures due to using the PLCC technology is the other advantage
of the proposed detection method. This characteristic is remarked as “cost-effective” in Table 3.
In addition, the simultaneous detection of both the operational and topological status of the network
in an online manner can be considered as an advantage of the proposed method. Finally, as a vital advantage,
the proposed detection method can be extended to more possible scenarios of the network such as double or
multiple line outage contingencies just by defining the related fuzzy-based rules in the FIS to cover all
possible outage contingencies that can be considered for future works.
Table 3. Characteristics comparison of the existing methods and the proposed method
Characteristic
Detection methods
Proposed
method
Operational Topological
Non-phasor
measurement-based
[6]–[15]
Phasor
measurement-
based [16]–[18]
Non-phasor
measurement-
based [19]
Phasor measurement-
based [20]-[34]
Less computational burden
Less complexity in
analyzing data
Cost-effective
: In accordance with the characteristic : Not in accordance with the characteristic
5. CONCLUSION
This paper proposed a scheme to detect the operational changes and line outage contingencies using
local data obtained from existing measurement devices. These data are analyzed at the control center and
utilized to detect the occurrence of line outage or changes in the network’s operation mode. One of the
advantages of the proposed scheme is the simultaneous detection of both operational and topological
changes. Also, the proposed scheme reduces the complexity of the existing detection methods and the
computational burden utilizing the FIS as an AI tool. Moreover, as a vital advantage, the proposed detection
scheme is based on transferring data through the existing power lines and there is no need for extra devices
for implementing wireless and Ethernet technologies or PMUs which makes the proposed scheme cost-
effective. To evaluate the proposed comprehensive detection scheme, MATLAB software is utilized to
simulate the FIS. As a consequence of the results obtained from the FIS, the proposed detection scheme can
successfully determine the accurate operational and topological status of the network based on real-time data
in an online manner.
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BIOGRAPHIES OF AUTHORS
Amin Damanjani received the B.Sc. degree in electrical power engineering from
Islamic Azad University, Bojnourd Branch, Bojnourd, Iran, in 2011, the M.Sc. degree in
electrical power engineering from Islamic Azad University, Gonabad Branch, Gonabad, Iran,
in 2014, and is currently pursuing the Ph.D. degree in electrical power engineering at Islamic
Azad University, Shahrood Branch, Shahrood, Iran. His research interests are power system
protection, power system operation, and power system analysis. He can be contacted at email:
amin.damanjani@iau-shahrood.ac.ir. Web of Science ResearcherID: ABG-8430-2021.
Mohamad Hosseini Abardeh received the B.Sc. degree in control engineering
from Ferdowsi University of Mashhad, Iran, in 2005, the M.Sc. degree in electrical power
engineering from Tarbiat Modares University of Tehran, Iran, in 2008, and the Ph.D. degree
in electrical power engineering from Ferdowsi University of Mashhad, Iran, in 2014. He is
currently working as Assistant Professor at the department of electrical and computer
engineering of Islamic Azad University, Shahrood Branch, Shahrood, Iran. His research
interests are power electronics, power quality, and renewable energies. He can be contacted at
email: mohamad.hosseini@mail.um.ac.ir. Web of Science ResearcherID: AAN-9178-2021.
14. Int J Elec & Comp Eng ISSN: 2088-8708
A comprehensive fuzzy-based scheme for online detection of operational … (Amin Damanjani)
3409
Azita Azarfar received the B.Sc. degree in biomedical engineering from
Amirkabir University of Technology, Tehran, Iran, in 2008, the M.Sc. degree in electrical
engineering from Shahrood University of Technology, Shahrood, Iran, in 2010, and the Ph.D.
degree in electrical engineering from Shahrood University of Technology, Shahrood, Iran, in
2014. She is currently working as Assistant Professor at the department of electrical and
computer engineering of Islamic Azad University, Shahrood Branch, Shahrood, Iran. Her
research interests are robotics, nonlinear control, intelligent control, and machine learning.
She can be cintacted at email: a_azarfar@iau-shahrood.ac.ir. Web of Science ResearcherID:
ABA-3681-2021.
Mehrdad Hojjat received the B.Sc. degree in electrical engineering from Power
and Water University of Technology, Iran, in 2006, the M.Sc. degree in electrical power
systems engineering from Ferdowsi University of Mashhad, Iran, in 2008, and the Ph.D.
degree in electrical power systems engineering from Ferdowsi University of Mashhad, Iran, in
2013. He is currently working as Assistant Professor at the department of electrical and
computer engineering of Islamic Azad University, Shahrood Branch, Shahrood, Iran. His
research interests are distributed generation, congestion management, power system analysis,
and renewable energies. He can be contacted at email: mehrdad.hojat@iau-shahrood.ac.ir.