This document presents a risk assessment method for power system transient stability that incorporates renewable energy sources. The method uses Gaussian process regression and feature selection algorithms to build a predictive model for online transient stability assessment. Offline data is collected from simulations at different operating conditions and contingencies. Feature selection algorithms identify the most important features related to critical fault clearing time as the stability index. The predictive model based on the selected features can then assess transient stability online by predicting critical fault clearing times based on new operating conditions. The method was tested on a 66-bus power system model with wind and solar power integrated at various buses.
Data-Driven Security Assessment of Power Grids Based on Machine Learning Appr...Power System Operation
Security assessment is a fundamental function for both short-term and long-term power system operations. The data-driven security assessment (DSA) can provide system stability margin without the need for detailed dynamic simulation. DSA is very helpful for control room applications such as online security assessment and day ahead or real-time dispatch scheduling with regard to system security constraints.
This paper investigates a data-driven security assessment of electric power grids based on machine learning. Multivariate random forest regression is used as the machine learning algorithm because of its high robustness to the input data. Three stability issues are analyzed using the proposed machine learning tool: transient stability, frequency stability, and small-signal stability. The estimation values from the machine learning tool are compared with those from dynamic simulations. Results show that the proposed machine learning tool can effectively predict the stability margins for the aforementioned three stabilities.
Data-Driven Security Assessment of Power Grids Based on Machine Learning Appr...Power System Operation
Security assessment is a fundamental function for both short-term and long-term power system operations. The data-driven security assessment (DSA) can provide system stability margin without the need for detailed dynamic simulation. DSA is very helpful for control room applications such as online security assessment and day ahead or real-time dispatch scheduling with regard to system security constraints.
Phase Measurement Units based FACT’s Devices for the Improvement of Power Sys...IJECEIAES
This paper describes the importance of FACTS devices; it presents the outcome of the study of its reflectance on the performance of power system networks. It seeks to increase and guarantee the fact and accuracy of response systems under disturbance conditions when the phase measurement units are introduced as Real-Time Measurement (RTM) stations. This paper also describes the importance of FACTS devices. The combination of FACTS devices and PMUs is presented to increase the controllability performance of power systems. This paper demonstrates how PMUs measure voltage, current and their angles. It provides, through a communication link, a Phase Angle Data Concentrator (PDC) to make an appropriate decision to correct the power system state using the FACTS device (TCSC). We utilized the Graph-Theoretic Algorithm to optimize the number and location of PMUs. The technique proposed was tested on the Iraqi National Super Grid’s 24bus network, Diyala City’s regional 10bus network and the 14bus IEEE standard test system. The MATLAB/PSAT package was utilized for the simulation of results. It is evident that our proposed algorithm and technique achieved the purpose of this paper as confirmed by the level of accuracy of the results obtained from most of the cases tested.
Improved predictive current model control based on adaptive PR controller for...IJECEIAES
This paper investigates an improved current predictive model control (PCMC) strategy with a prediction horizon of one sampling time for voltage regulation in standalone system based on diesel engine driven fixed speed synchronous generator. An adaptive PR controller with anti-windup scheme is employed to achieve high performance regulation without saturation issues. In addition, new method to obtain the optimal parameters of the adaptive PR controller to achieve high performance during the transition and in steady state, is provided. Furthermore, to balance the power at the point of common coupling (PCC), as well as, to fulfilling a clean power to the connected loads, a three-phase voltage source inverter (VSI) with LRC filter is controlled using the developed improved PCMC strategy. The output filter current is controlled using the predicting of the system behaviour model in the future step, at each sampling prediction time. The performances of the developed control strategy are verified using Matlab/Simulink interface.
This document discusses electrical energy management and load forecasting in smart grids using artificial neural networks. It presents a study applying backpropagation neural networks to short-term load forecasting for Sudan's National Electric Company. The neural network model was used to forecast load, with error calculated by comparing forecasted and actual load data. The document also discusses generation dispatch, demand forecasting techniques, and designing a neural network for one-day load forecasting. It evaluates network performance and error for different training data sizes, finding that a ten-day training dataset produced the best results with minimum error. The neural network approach was able to reliably predict the nonlinear relationship between historical data and load.
Cluster Computing Environment for On - line Static Security Assessment of lar...IDES Editor
The increased size of modern power systems
demand faster and accurate means for the security assessment,
so that the decisions for reliable and secure operation planning
could be drawn in a systematic manner. Large computational
overhead is the major impediment in preventing the power
system security assessment (PSSA) from on-line use. To
mitigate this problem, this paper proposes, a cluster computing
based architecture for power system static security assessment,
utilizing the tools in the open source domain. A variant of the
master/slave pattern is used for deploying the cluster of
workstations (COW), which act as the computational engine
for the on-line PSSA. The security assessment is performed
utilizing the developed composite security index that can
accurately differentiate the secure and non-secure cases and
has been defined as a function of bus voltage and line flow
limit violations. Due to the inherent parallel structure of
security assessment algorithm and to exploit the potential of
distributed computing, domain decomposition is employed for
parallelizing the sequential algorithm. Extensive
experimentations were carried out on IEEE 57 bus and IEEE
145-bus 50 machine standard test systems for demonstrating
the validity of the proposed architecture.
On-line Power System Static Security Assessment in a Distributed Computing Fr...idescitation
The computation overhead is of major concern when
going for increased accuracy in online power system security
assessment (OPSSA). This paper proposes a scalable solution
technique based on distributed computing architecture to
mitigate the problem. A variant of the master/slave pattern is
used for deploying the cluster of workstations (COW), which
act as the computational engine for the OPSSA. Owing to the
inherent parallel structure in security analysis algorithm, to
exploit the potential of distributed computing, domain
decomposition is adopted instead of functional decomposition.
The security assessment is performed utilizing the developed
composite security index that can accurately differentiate the
secure and non-secure cases and has been defined as a function
of bus voltage and line flow limit violations. Validity of
proposed architecture is demonstrated by the results obtained
from an intensive experimentation using the benchmark IEEE
57 bus test system. The proposed framework, which is scalable,
can be further extended to intelligent monitoring and control
of power system
Overview of State Estimation Technique for Power System ControlIOSR Journals
This document provides an overview of state estimation techniques for power system control. It discusses static, tracking, and dynamic state estimation approaches and how they differ based on whether measurements and system models are time-variant or invariant. The document also describes how state estimation processes redundant measurements to filter noise and estimate true system states like voltage magnitudes and angles at each bus. It further discusses weighted least squares estimation, the use of Jacobian matrices to iteratively estimate states, and how state estimation provides critical real-time data for power system monitoring and control functions.
Data-Driven Security Assessment of Power Grids Based on Machine Learning Appr...Power System Operation
Security assessment is a fundamental function for both short-term and long-term power system operations. The data-driven security assessment (DSA) can provide system stability margin without the need for detailed dynamic simulation. DSA is very helpful for control room applications such as online security assessment and day ahead or real-time dispatch scheduling with regard to system security constraints.
This paper investigates a data-driven security assessment of electric power grids based on machine learning. Multivariate random forest regression is used as the machine learning algorithm because of its high robustness to the input data. Three stability issues are analyzed using the proposed machine learning tool: transient stability, frequency stability, and small-signal stability. The estimation values from the machine learning tool are compared with those from dynamic simulations. Results show that the proposed machine learning tool can effectively predict the stability margins for the aforementioned three stabilities.
Data-Driven Security Assessment of Power Grids Based on Machine Learning Appr...Power System Operation
Security assessment is a fundamental function for both short-term and long-term power system operations. The data-driven security assessment (DSA) can provide system stability margin without the need for detailed dynamic simulation. DSA is very helpful for control room applications such as online security assessment and day ahead or real-time dispatch scheduling with regard to system security constraints.
Phase Measurement Units based FACT’s Devices for the Improvement of Power Sys...IJECEIAES
This paper describes the importance of FACTS devices; it presents the outcome of the study of its reflectance on the performance of power system networks. It seeks to increase and guarantee the fact and accuracy of response systems under disturbance conditions when the phase measurement units are introduced as Real-Time Measurement (RTM) stations. This paper also describes the importance of FACTS devices. The combination of FACTS devices and PMUs is presented to increase the controllability performance of power systems. This paper demonstrates how PMUs measure voltage, current and their angles. It provides, through a communication link, a Phase Angle Data Concentrator (PDC) to make an appropriate decision to correct the power system state using the FACTS device (TCSC). We utilized the Graph-Theoretic Algorithm to optimize the number and location of PMUs. The technique proposed was tested on the Iraqi National Super Grid’s 24bus network, Diyala City’s regional 10bus network and the 14bus IEEE standard test system. The MATLAB/PSAT package was utilized for the simulation of results. It is evident that our proposed algorithm and technique achieved the purpose of this paper as confirmed by the level of accuracy of the results obtained from most of the cases tested.
Improved predictive current model control based on adaptive PR controller for...IJECEIAES
This paper investigates an improved current predictive model control (PCMC) strategy with a prediction horizon of one sampling time for voltage regulation in standalone system based on diesel engine driven fixed speed synchronous generator. An adaptive PR controller with anti-windup scheme is employed to achieve high performance regulation without saturation issues. In addition, new method to obtain the optimal parameters of the adaptive PR controller to achieve high performance during the transition and in steady state, is provided. Furthermore, to balance the power at the point of common coupling (PCC), as well as, to fulfilling a clean power to the connected loads, a three-phase voltage source inverter (VSI) with LRC filter is controlled using the developed improved PCMC strategy. The output filter current is controlled using the predicting of the system behaviour model in the future step, at each sampling prediction time. The performances of the developed control strategy are verified using Matlab/Simulink interface.
This document discusses electrical energy management and load forecasting in smart grids using artificial neural networks. It presents a study applying backpropagation neural networks to short-term load forecasting for Sudan's National Electric Company. The neural network model was used to forecast load, with error calculated by comparing forecasted and actual load data. The document also discusses generation dispatch, demand forecasting techniques, and designing a neural network for one-day load forecasting. It evaluates network performance and error for different training data sizes, finding that a ten-day training dataset produced the best results with minimum error. The neural network approach was able to reliably predict the nonlinear relationship between historical data and load.
Cluster Computing Environment for On - line Static Security Assessment of lar...IDES Editor
The increased size of modern power systems
demand faster and accurate means for the security assessment,
so that the decisions for reliable and secure operation planning
could be drawn in a systematic manner. Large computational
overhead is the major impediment in preventing the power
system security assessment (PSSA) from on-line use. To
mitigate this problem, this paper proposes, a cluster computing
based architecture for power system static security assessment,
utilizing the tools in the open source domain. A variant of the
master/slave pattern is used for deploying the cluster of
workstations (COW), which act as the computational engine
for the on-line PSSA. The security assessment is performed
utilizing the developed composite security index that can
accurately differentiate the secure and non-secure cases and
has been defined as a function of bus voltage and line flow
limit violations. Due to the inherent parallel structure of
security assessment algorithm and to exploit the potential of
distributed computing, domain decomposition is employed for
parallelizing the sequential algorithm. Extensive
experimentations were carried out on IEEE 57 bus and IEEE
145-bus 50 machine standard test systems for demonstrating
the validity of the proposed architecture.
On-line Power System Static Security Assessment in a Distributed Computing Fr...idescitation
The computation overhead is of major concern when
going for increased accuracy in online power system security
assessment (OPSSA). This paper proposes a scalable solution
technique based on distributed computing architecture to
mitigate the problem. A variant of the master/slave pattern is
used for deploying the cluster of workstations (COW), which
act as the computational engine for the OPSSA. Owing to the
inherent parallel structure in security analysis algorithm, to
exploit the potential of distributed computing, domain
decomposition is adopted instead of functional decomposition.
The security assessment is performed utilizing the developed
composite security index that can accurately differentiate the
secure and non-secure cases and has been defined as a function
of bus voltage and line flow limit violations. Validity of
proposed architecture is demonstrated by the results obtained
from an intensive experimentation using the benchmark IEEE
57 bus test system. The proposed framework, which is scalable,
can be further extended to intelligent monitoring and control
of power system
Overview of State Estimation Technique for Power System ControlIOSR Journals
This document provides an overview of state estimation techniques for power system control. It discusses static, tracking, and dynamic state estimation approaches and how they differ based on whether measurements and system models are time-variant or invariant. The document also describes how state estimation processes redundant measurements to filter noise and estimate true system states like voltage magnitudes and angles at each bus. It further discusses weighted least squares estimation, the use of Jacobian matrices to iteratively estimate states, and how state estimation provides critical real-time data for power system monitoring and control functions.
Optimized Predictive Control for AGC Cyber Resiliency.pdfJ. A. Laghari
This document proposes an approach for detecting and mitigating cyber attacks on automatic generation control (AGC) systems using optimized predictive control and prior information. The approach uses Gaussian process regression to forecast measurements and detect anomalies by comparing forecasts to real measurements. It was tested using a simulator based on the IEEE 39-bus model and modified load data, showing it can detect attacks with 100% accuracy and mitigate impacts to maintain low estimation errors under attacks.
Transient Stability Assessment and Enhancement in Power SystemIJMER
This document discusses transient stability assessment and enhancement in power systems. It first introduces transient stability and its importance. It then describes using PSAT software to analyze the IEEE 39-bus test system and calculate critical clearing times (CCTs) for different faults to assess stability. An artificial neural network is trained to predict CCTs at different operating points. Finally, particle swarm optimization is used to find the optimal placement of a thyristor controlled series capacitor to enhance stability by minimizing real power losses, increasing several CCTs above 0.1 seconds.
Study on the performance indicators for smart grids: a comprehensive reviewTELKOMNIKA JOURNAL
This paper presents a detailed review on performance indicators for smart grid (SG) such as voltage stability enhancement, reliability evaluation, vulnerability assessment, Supervisory Control and Data Acquisition (SCADA) and communication systems. Smart grids reliability assessment can be performed by analytically or by simulation. Analytical method utilizes the load point assessment techniques, whereas the simulation technique uses the Monte Carlo simulation (MCS) technique. The reliability index evaluations will consider the presence or absence of energy storage elements using the simulation technologies such as MCS, and the analytical methods such as systems average interruption frequency index (SAIFI), and other load point indices. This paper also presents the difference between SCADA and substation automation, and the fact that substation automation, though it uses the basic concepts of SCADA, is far more advanced in nature.
IRJET- Voltage Stability, Loadability and Contingency Analysis with Optimal I...IRJET Journal
This document discusses contingency analysis and optimal placement of renewable distributed generators (RDGs) using continuation power flow analysis to improve voltage stability and loadability. It presents a methodology to determine the optimal location and mix of different RDG technologies (solar, wind, fuel cells) on the IEEE 9-bus test system using the Power System Analysis Toolbox (PSAT). Reactive power performance indices are calculated for different line contingencies to identify critical buses. The results show that optimally placing RDGs can enhance voltage stability and increase the maximum loadability point compared to the base case without RDGs.
A NEW TOOL FOR LARGE SCALE POWER SYSTEM TRANSIENT SECURITY ASSESSMENTPower System Operation
This document proposes a new method for fast assessment of transient security power limits in large power systems using neural networks. It establishes a nonlinear mapping between transient energy margin and generator power at different fault clearing times and load levels using a self-organizing map. The transient security power limits of generators can then be estimated very quickly by inputting fault clearing time and load level data. Testing on a sample power system shows the proposed method can accurately estimate security limits without needing to calculate analytical sensitivities, providing faster results than traditional methods.
Studies enhancement of transient stability by single machine infinite bus sys...nooriasukmaningtyas
Maintaining network synchronization is important to customer service. Low fluctuations cause voltage instability, non-synchronization in the power system or the problems in the electrical system disturbances, harmonics current and voltages inflation and contraction voltage. Proper tunning of the parameters of stabilizer is prime for validation of stabilizer. To overcome instability issues and get reinforcement found a lot of the techniques are developed to overcome instability problems and improve performance of power system. Genetic algorithm was applied to optimize parameters and suppress oscillation. The simulation of the robust composite capacitance system of an infinite single-machine bus was studied using MATLAB was used for optimization purpose. The critical time is an indication of the maximum possible time during which the error can pass in the system to obtain stability through the simulation. The effectiveness improvement has been shown in the system
Ga based optimal facts controller for maximizing loadability with stability c...IAEME Publication
This document summarizes a research paper presented at the International Conference on Emerging Trends in Engineering and Management. The paper proposes using a genetic algorithm to determine the optimal location and settings of Flexible AC Transmission System (FACTS) devices, specifically STATCOMs, to maximize the loadability of a power system while maintaining stability constraints. The objective function aims to maximize loadability with constraints for voltage stability, generation limits, line limits, and load-generation balance. The methodology is tested on the IEEE 14-bus test system in MATLAB. In conclusion, optimally placing and setting FACTS devices using genetic algorithms can enhance power system loadability while maintaining stability.
A new linear quadratic regulator model to mitigate frequency disturbances in...IJECEIAES
This paper proposes a new model integrating a linear quadratic regulator (LQR) controller to mitigate frequency disturbances in the power system during cyber-attack, called as linear quadratic regulator to mitigate frequency disturbances (LQRMFD). As we know, most of the existing models have a common problem with achieving significant performances in mitigating dynamic response parameters, such as frequency deviation and settling time. However, the key aspect of LQRMFD is to mitigate the above issues with remarkable performance improvements. An uncommon and stable power system model has been considered in LQRMFD first to reach such a goal. A numerical problem has been solved to derive a certain characteristic equation, where the Routh-Hurwitz array criterion is applied for determining the stability of such a power system. After that, a state-space equation is developed from the power system to activate the LQR controller. Thus, achieving diversity and eliminating the redundancy of the power system considered can be obtained in LQRMFD. To evaluate the performance of LQRMFD, a series of experiments was conducted using the MATLABSimulink tool. Rigorous comparisons were also made among the results of LQRMFD, self-implemented and existing models. Furthermore, a detailed analysis was reported among those models to find the performance improvement of LQRMFD in percentage.
Neural Network-Based Stabilizer for the Improvement of Power System Dynamic P...TELKOMNIKA JOURNAL
This paper develops an adaptive control coordination scheme for power system stabilizers (PSSs)
to improve the oscillation damping and dynamic performance of interconnected multimachine power
system. The scheme was based on the use of a neural network which identifies online the optimal
controller parameters. The inputs to the neural network include the active- and reactive- power of the
synchronous generators which represent the power loading on the system, and elements of the reduced
nodal impedance matrix for representing the power system configuration. The outputs of the neural
network were the parameters of the PSSs which lead to optimal oscillation damping for the prevailing
system configuration and operating condition. For a representative power system, the neural network has
been trained and tested for a wide range of credible operating conditions and contingencies. Both
eigenvalue calculations and time-domain simulations were used in the testing and verification of the
performance of the neural network-based stabilizer.
Robust control for a tracking electromechanical systemIJECEIAES
A strategy for the design of robust control of tracking electromechanical systems based on 𝐻∞ synthesis is proposed. Proposed methods are based on the operations on frequency characteristics of control systems designed and developed using the MATLAB robust control toolbox. Determination of the singular values for a transfer matrix of the control system reduces the disturbances and guarantees its stability margin. For selecting the weighted transfer functions, the basic recommendations are formulated. The efficiency of the proposed approach is verified by robust control of an elastically coupled two-mass system whose parameter values are adjusted by matching them with the parameters of one of the supplied robots. The simulation results confirm that the proposed strategy of design of robust control of twomass elastic coupling system using the 𝐻∞ synthesis is very efficient and significantly reduces the perturbation of parameters of the controlled plant.
Predicting Post Outage Transmission Line Flows using Linear Distribution FactorsDr. Amarjeet Singh
In order to design and implement preventive
and remedial actions, a continuous performance of fast
security analysis is imperative amid outages of system
components. Following the contingency of a system
component, State estimation and Load flow techniques
are the two popular techniques used to determine
system state variables leading to estimation of flows,
losses and violations in nodal voltages and transmission
line flows. But the dynamic state and complexity of the
system requires faster means of estimations which can
be achieved by linear distribution factors. The use of
Distribution factors in form of Power Transfer
Distribution Factors (PTDF) and Line Outage
Distribution Factors (LODF) which are transmission
line sensitivities with respect to active power exchanges
between buses and transmission line outages offer an
alternative to these two techniques being linear,
quicker, and non-iterative. Following the estimation of
the linear distribution factors from a reference
operating point (base case) and contingency cases
involving line outage, generator output variation and
outage of a Six bus network using Matlab programs,
the results show that by means of Linear Distribution
factors quick estimates of post outage line flows can be
made which match flow results obtained from DC load
flow analysis.
Joint State and Parameter Estimation by Extended Kalman Filter (EKF) techniqueIJERD Editor
In order to increase power system stability and reliability during and after disturbances, power grid
global and local controllers must be developed. SCADA system provides steady and low sampling density. To
remove these limitation PMUs are being rapidly adopted worldwide. Dynamic states of power system can be
estimated using EKF. This requires field excitation as input which may not available. As a result, the EKF with
unknown inputs proposed for identifying and estimating the states and the unknown inputs of the synchronous
machine.
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
IRJET- A Review on SVM based Induction MotorIRJET Journal
This document summarizes several research papers on using support vector machines (SVMs) and other machine learning techniques for fault detection in induction motors. Specifically:
1. It discusses using an artificial immune system-optimized SVM for detecting broken rotor bars and stator faults in induction motors based on motor current data.
2. It describes using wavelet analysis, principal component analysis, and SVM classification to detect faults like frequency variations, unbalanced voltages, and interturn shorts based on motor current spectra.
3. It proposes using dq0 voltage components analyzed with fast Fourier transforms as features for an SVM classifier to detect stator winding shorts, achieving over 98% accuracy.
This document describes a simulation model developed for testing microgrid and distributed energy resource (DER) control methods. The simulation model was built in PSIM software to emulate a hardware test bench consisting of a synchronous generator, DC motor prime mover, and CPT E13 inverter controller. The simulation allows development of automatic voltage regulation (AVR) and governor control methods to regulate the generator's output voltage and frequency. Software controllers developed using the simulation model were then tested on the physical hardware system to validate the simulation as a design tool.
Application of artificial intelligence in early fault detection of transmiss...IJECEIAES
This document discusses applying artificial intelligence techniques like artificial neural networks to detect faults early in transmission lines. It analyzes common faults that occurred on a 400kV transmission line in India in 2020. The techniques aim to identify faults, classify them, and determine the best maintenance approach, whether live line maintenance or taking an outage. Statistical analysis shows combining ANN with live line maintenance can minimize outage time and failures compared to cold line maintenance, improving system availability.
Steady state stability analysis and enhancement of three machine nine bus pow...eSAT Journals
This document presents an analysis of steady state stability for the IEEE 3-machine 9-bus test power system. It first describes the mathematical modeling of the system using linearization and state space representation. Eigenvalue analysis shows the system is purely oscillatory without damping. A thyristor controlled phase shifter (TCPS) FACTS device-based controller is then modeled to enhance stability. The system is analyzed with and without the controller. Results show the controller provides damping, improving stability as seen in eigenvalue analysis and time domain simulations following a disturbance.
A Comprehensive review on Optimization Algorithms for Best Location of FACTS ...IRJET Journal
This document reviews 59 studies on optimization algorithms for determining the optimal placement of flexible AC transmission system (FACTS) controllers. It categorizes the algorithms into analytical optimization techniques using sensitivity analysis, metaheuristic optimization techniques including evolutionary algorithms like genetic algorithm and differential evolution, and swarm-based algorithms like particle swarm optimization. It summarizes several studies applying these various techniques to test cases like IEEE 5, 14, 30 bus systems to optimally place FACTS controllers like thyristor controlled series compensator (TCSC), static VAR compensator (SVC), unified power flow controller (UPFC) with objectives of minimizing losses, improving voltage profile, enhancing security and loadability.
Adaptive Control strategies helps to get desirable output for system with partial unknown dynamics or systems having unknown and unmodeled load variation. DC servo motors are useful to track rapid speed trajectory for various applications, particularly with need of high starting torque and low inertia. Model Reference Adaptive Control (MRAC) parameter data of results with Lyapunov stability MRAC has been used to generate adaptation parameter for DC motor speed controller.
A transition from manual to Intelligent Automated power system operation -A I...IJECEIAES
This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical.
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 proposes an approach for detecting and mitigating cyber attacks on automatic generation control (AGC) systems using optimized predictive control and prior information. The approach uses Gaussian process regression to forecast measurements and detect anomalies by comparing forecasts to real measurements. It was tested using a simulator based on the IEEE 39-bus model and modified load data, showing it can detect attacks with 100% accuracy and mitigate impacts to maintain low estimation errors under attacks.
Transient Stability Assessment and Enhancement in Power SystemIJMER
This document discusses transient stability assessment and enhancement in power systems. It first introduces transient stability and its importance. It then describes using PSAT software to analyze the IEEE 39-bus test system and calculate critical clearing times (CCTs) for different faults to assess stability. An artificial neural network is trained to predict CCTs at different operating points. Finally, particle swarm optimization is used to find the optimal placement of a thyristor controlled series capacitor to enhance stability by minimizing real power losses, increasing several CCTs above 0.1 seconds.
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IRJET- Voltage Stability, Loadability and Contingency Analysis with Optimal I...IRJET Journal
This document discusses contingency analysis and optimal placement of renewable distributed generators (RDGs) using continuation power flow analysis to improve voltage stability and loadability. It presents a methodology to determine the optimal location and mix of different RDG technologies (solar, wind, fuel cells) on the IEEE 9-bus test system using the Power System Analysis Toolbox (PSAT). Reactive power performance indices are calculated for different line contingencies to identify critical buses. The results show that optimally placing RDGs can enhance voltage stability and increase the maximum loadability point compared to the base case without RDGs.
A NEW TOOL FOR LARGE SCALE POWER SYSTEM TRANSIENT SECURITY ASSESSMENTPower System Operation
This document proposes a new method for fast assessment of transient security power limits in large power systems using neural networks. It establishes a nonlinear mapping between transient energy margin and generator power at different fault clearing times and load levels using a self-organizing map. The transient security power limits of generators can then be estimated very quickly by inputting fault clearing time and load level data. Testing on a sample power system shows the proposed method can accurately estimate security limits without needing to calculate analytical sensitivities, providing faster results than traditional methods.
Studies enhancement of transient stability by single machine infinite bus sys...nooriasukmaningtyas
Maintaining network synchronization is important to customer service. Low fluctuations cause voltage instability, non-synchronization in the power system or the problems in the electrical system disturbances, harmonics current and voltages inflation and contraction voltage. Proper tunning of the parameters of stabilizer is prime for validation of stabilizer. To overcome instability issues and get reinforcement found a lot of the techniques are developed to overcome instability problems and improve performance of power system. Genetic algorithm was applied to optimize parameters and suppress oscillation. The simulation of the robust composite capacitance system of an infinite single-machine bus was studied using MATLAB was used for optimization purpose. The critical time is an indication of the maximum possible time during which the error can pass in the system to obtain stability through the simulation. The effectiveness improvement has been shown in the system
Ga based optimal facts controller for maximizing loadability with stability c...IAEME Publication
This document summarizes a research paper presented at the International Conference on Emerging Trends in Engineering and Management. The paper proposes using a genetic algorithm to determine the optimal location and settings of Flexible AC Transmission System (FACTS) devices, specifically STATCOMs, to maximize the loadability of a power system while maintaining stability constraints. The objective function aims to maximize loadability with constraints for voltage stability, generation limits, line limits, and load-generation balance. The methodology is tested on the IEEE 14-bus test system in MATLAB. In conclusion, optimally placing and setting FACTS devices using genetic algorithms can enhance power system loadability while maintaining stability.
A new linear quadratic regulator model to mitigate frequency disturbances in...IJECEIAES
This paper proposes a new model integrating a linear quadratic regulator (LQR) controller to mitigate frequency disturbances in the power system during cyber-attack, called as linear quadratic regulator to mitigate frequency disturbances (LQRMFD). As we know, most of the existing models have a common problem with achieving significant performances in mitigating dynamic response parameters, such as frequency deviation and settling time. However, the key aspect of LQRMFD is to mitigate the above issues with remarkable performance improvements. An uncommon and stable power system model has been considered in LQRMFD first to reach such a goal. A numerical problem has been solved to derive a certain characteristic equation, where the Routh-Hurwitz array criterion is applied for determining the stability of such a power system. After that, a state-space equation is developed from the power system to activate the LQR controller. Thus, achieving diversity and eliminating the redundancy of the power system considered can be obtained in LQRMFD. To evaluate the performance of LQRMFD, a series of experiments was conducted using the MATLABSimulink tool. Rigorous comparisons were also made among the results of LQRMFD, self-implemented and existing models. Furthermore, a detailed analysis was reported among those models to find the performance improvement of LQRMFD in percentage.
Neural Network-Based Stabilizer for the Improvement of Power System Dynamic P...TELKOMNIKA JOURNAL
This paper develops an adaptive control coordination scheme for power system stabilizers (PSSs)
to improve the oscillation damping and dynamic performance of interconnected multimachine power
system. The scheme was based on the use of a neural network which identifies online the optimal
controller parameters. The inputs to the neural network include the active- and reactive- power of the
synchronous generators which represent the power loading on the system, and elements of the reduced
nodal impedance matrix for representing the power system configuration. The outputs of the neural
network were the parameters of the PSSs which lead to optimal oscillation damping for the prevailing
system configuration and operating condition. For a representative power system, the neural network has
been trained and tested for a wide range of credible operating conditions and contingencies. Both
eigenvalue calculations and time-domain simulations were used in the testing and verification of the
performance of the neural network-based stabilizer.
Robust control for a tracking electromechanical systemIJECEIAES
A strategy for the design of robust control of tracking electromechanical systems based on 𝐻∞ synthesis is proposed. Proposed methods are based on the operations on frequency characteristics of control systems designed and developed using the MATLAB robust control toolbox. Determination of the singular values for a transfer matrix of the control system reduces the disturbances and guarantees its stability margin. For selecting the weighted transfer functions, the basic recommendations are formulated. The efficiency of the proposed approach is verified by robust control of an elastically coupled two-mass system whose parameter values are adjusted by matching them with the parameters of one of the supplied robots. The simulation results confirm that the proposed strategy of design of robust control of twomass elastic coupling system using the 𝐻∞ synthesis is very efficient and significantly reduces the perturbation of parameters of the controlled plant.
Predicting Post Outage Transmission Line Flows using Linear Distribution FactorsDr. Amarjeet Singh
In order to design and implement preventive
and remedial actions, a continuous performance of fast
security analysis is imperative amid outages of system
components. Following the contingency of a system
component, State estimation and Load flow techniques
are the two popular techniques used to determine
system state variables leading to estimation of flows,
losses and violations in nodal voltages and transmission
line flows. But the dynamic state and complexity of the
system requires faster means of estimations which can
be achieved by linear distribution factors. The use of
Distribution factors in form of Power Transfer
Distribution Factors (PTDF) and Line Outage
Distribution Factors (LODF) which are transmission
line sensitivities with respect to active power exchanges
between buses and transmission line outages offer an
alternative to these two techniques being linear,
quicker, and non-iterative. Following the estimation of
the linear distribution factors from a reference
operating point (base case) and contingency cases
involving line outage, generator output variation and
outage of a Six bus network using Matlab programs,
the results show that by means of Linear Distribution
factors quick estimates of post outage line flows can be
made which match flow results obtained from DC load
flow analysis.
Joint State and Parameter Estimation by Extended Kalman Filter (EKF) techniqueIJERD Editor
In order to increase power system stability and reliability during and after disturbances, power grid
global and local controllers must be developed. SCADA system provides steady and low sampling density. To
remove these limitation PMUs are being rapidly adopted worldwide. Dynamic states of power system can be
estimated using EKF. This requires field excitation as input which may not available. As a result, the EKF with
unknown inputs proposed for identifying and estimating the states and the unknown inputs of the synchronous
machine.
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
IRJET- A Review on SVM based Induction MotorIRJET Journal
This document summarizes several research papers on using support vector machines (SVMs) and other machine learning techniques for fault detection in induction motors. Specifically:
1. It discusses using an artificial immune system-optimized SVM for detecting broken rotor bars and stator faults in induction motors based on motor current data.
2. It describes using wavelet analysis, principal component analysis, and SVM classification to detect faults like frequency variations, unbalanced voltages, and interturn shorts based on motor current spectra.
3. It proposes using dq0 voltage components analyzed with fast Fourier transforms as features for an SVM classifier to detect stator winding shorts, achieving over 98% accuracy.
This document describes a simulation model developed for testing microgrid and distributed energy resource (DER) control methods. The simulation model was built in PSIM software to emulate a hardware test bench consisting of a synchronous generator, DC motor prime mover, and CPT E13 inverter controller. The simulation allows development of automatic voltage regulation (AVR) and governor control methods to regulate the generator's output voltage and frequency. Software controllers developed using the simulation model were then tested on the physical hardware system to validate the simulation as a design tool.
Application of artificial intelligence in early fault detection of transmiss...IJECEIAES
This document discusses applying artificial intelligence techniques like artificial neural networks to detect faults early in transmission lines. It analyzes common faults that occurred on a 400kV transmission line in India in 2020. The techniques aim to identify faults, classify them, and determine the best maintenance approach, whether live line maintenance or taking an outage. Statistical analysis shows combining ANN with live line maintenance can minimize outage time and failures compared to cold line maintenance, improving system availability.
Steady state stability analysis and enhancement of three machine nine bus pow...eSAT Journals
This document presents an analysis of steady state stability for the IEEE 3-machine 9-bus test power system. It first describes the mathematical modeling of the system using linearization and state space representation. Eigenvalue analysis shows the system is purely oscillatory without damping. A thyristor controlled phase shifter (TCPS) FACTS device-based controller is then modeled to enhance stability. The system is analyzed with and without the controller. Results show the controller provides damping, improving stability as seen in eigenvalue analysis and time domain simulations following a disturbance.
A Comprehensive review on Optimization Algorithms for Best Location of FACTS ...IRJET Journal
This document reviews 59 studies on optimization algorithms for determining the optimal placement of flexible AC transmission system (FACTS) controllers. It categorizes the algorithms into analytical optimization techniques using sensitivity analysis, metaheuristic optimization techniques including evolutionary algorithms like genetic algorithm and differential evolution, and swarm-based algorithms like particle swarm optimization. It summarizes several studies applying these various techniques to test cases like IEEE 5, 14, 30 bus systems to optimally place FACTS controllers like thyristor controlled series compensator (TCSC), static VAR compensator (SVC), unified power flow controller (UPFC) with objectives of minimizing losses, improving voltage profile, enhancing security and loadability.
Adaptive Control strategies helps to get desirable output for system with partial unknown dynamics or systems having unknown and unmodeled load variation. DC servo motors are useful to track rapid speed trajectory for various applications, particularly with need of high starting torque and low inertia. Model Reference Adaptive Control (MRAC) parameter data of results with Lyapunov stability MRAC has been used to generate adaptation parameter for DC motor speed controller.
A transition from manual to Intelligent Automated power system operation -A I...IJECEIAES
This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical.
Similar to Risk assessment of power system transient instability incorporating renewable energy sources (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%.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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%.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
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.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
Risk assessment of power system transient instability incorporating renewable energy sources
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 5, October 2022, pp. 4649~4660
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i5.pp4649-4660 4649
Journal homepage: http://ijece.iaescore.com
Risk assessment of power system transient instability
incorporating renewable energy sources
Ayman Hoballah, Salah Kamal El-Sayed, Sattam Al Otaibi, Essam Hendawi, Nagy Elkalashy,
Yasser Ahmed
Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia
Article Info ABSTRACT
Article history:
Received Dec 7, 2021
Revised Apr 7, 2022
Accepted May 5, 2022
Transient stability affected by renewable energy sources integration due to
reductions of system inertia and uncertainties associated with the expected
generation. The ability to manage relation between the available big data and
transient stability assessment (TSA) enables fast and accurate monitoring of
TSA to prepare the required actions for secure operation. This work aims to
build a predictive model using Gaussian process regression for online TSA
utilizing selected features. The critical fault clearing time (CCT) is used as
TSA index. The selected features map the system dynamics to reduce the
burden of data collection and the computation time. The required data were
collected offline from power flow calculations at different operating
conditions. Therefore, CCT was calculated using electromagnetic transient
simulation at each operating point by applying self-clearance three phase
short circuit at prespecified locations. The features selection was
implemented using the neighborhood component analysis, the Minimum
Redundancy Maximum Relevance algorithm, and K-means clustering
algorithm. The vulnerability of selected features tends to result great
variation on the best features from the three methods. Hybrid collection of
the best common features was used to enhance the TSA by refining the final
selected features. The proposed model was investigated over 66-bus system.
Keywords:
Feature selection
Gaussian process regression
Renewable energy sources
Statistical analysis
Transient stability assessment
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ayman Hoballah
Department of Electrical Engineering, College of Engineering, Taif University
P.O. Box 11099, Taif 21944, Saudi Arabia
Email: ayman.h@tu.edu.sa
1. INTRODUCTION
Fast transient stability assessment (TSA) enables the system operator to initiate the necessary
remedial action for enhancing system security during abnormal operating conditions. The load variations and
system topology changes as well as uncertain of generation levels of different renewable energy sources
(RES) may impulse the system towards the stability boundary following large disturbances [1], [2]. TSA of
large-scale power system depends on the synchronization among generators. However, it is necessary to
continuously evaluate TSA during online operation to prevent serious electromagnetic oscillations. The
computation burden of online TSA is a great challenge [3], [4]. Accurate TSA requires step by step time
domain simulation (TDS) of large number of nonlinear equations at pre-fault and post-fault trajectories. TSA
can be evaluated by monitoring the deviation between the rotor angles of synchronous generators which
required handling with collected big data. The generators are considered out of step when the rotor angle
deviations exceed the pre-specified limits following faults [5]. Therefore, the critical fault clearing time
(CCT) is considered as accurate indicator of the system transient stability [6], [7].
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Artificial intelligent and statistical analysis methods were applied to reduce the TSA computation
time using phasor measurement units (PMUs) [8]. The collected information from PMUs was utilized to
evaluate the deviation among rotor angles to design the out-of-step protection system avoiding system
collapse [9]. In research of Gomez et al. [10], support vector machines were used to predict TSA using
voltage magnitude measurements following faulty condition at balanced or unbalanced fault conditions. The
decision tree (DT) along with artificial neural network (ANN) were implemented to specify the required
counter-measurement to assess and enhance the power system transient stability. TDS was used to calculate
the CCT and the power flow information where DT was used to estimate the CCT based on selected
predictors to map the system dynamics. ANN was used to estimate the generating levels for economic
dispatch considering the system stability or initiate a prespecified correction actions based on the historical or
excremental information [11]. ANN was implemented for TSA by monitoring the rotor angle oscillations
among generators. The inputs were the phase angle differences and their rate of variations [12]. The success
of these schemes encourages the researchers to develop more systematic approaches for TSA tools to account
the continuous variation of operating conditions, uncertainties associated with the RES generating levels and
reduction in system inertia due to RES replacing traditional generating units [13]–[15].
In this paper, hybrid analytical method for TSA using predictive model based Gaussian process
regression (GPR). Therefore, the random variation of generation and loads were considered. The method
depends on the offline collected data from applying optimal power flow (OPF) for variety of operating
conditions where feature selection algorithms were used to reduce the data dimension. The GPR predictive
model is a nonparametric algorithm that depends on the calculation of the probability distribution over the
assumptive possible functions to fit the input and output data. GPR has the capability of cub complex
relationships by approximating the target function. GPR is being employed in many engineering applications.
2. PROPOSED METHOD AND CONTRIBUTION
The main steps of the proposed method for online TSA evaluation by using selected features based
on GPR predictive model can be summarized as follow:
− Step 1: The description of the photovoltaic (PV) systems and wind system dynamic models within the
DigSilent simulation software which was used to evaluate OPF calculations and the corresponding CCT
according to set of contingencies. Therefore, large number of datasets were collected during offline.
− Step 2: Different features selection algorithms (the neighborhood component analysis, the Minimum
Redundancy Maximum Relevance algorithm, and K-means clustering algorithm) were applied for data
mining to select the best features to map the system dynamics for constructing of TSA predictive model.
The results were compared to improve the accuracy of TSA predictive model.
− Step 3: GPR predictive model was built to estimate the CCT based on selected features. The GPR
predictive model was trained offline based on the selected features to predict the CCT as indicator for
TSA during online applications. The strong correlation between selected features and TSA indicator
reflects the importance of system stability monitoring to move away from stability boundary. The GPR
predictive model maps the relationship between the selected features and the CCT to predict the system
state of stability based on new values of selected features. The process of evaluations can be summarized
as follows: i) random variation of loads according to the expected loading levels and RES generation
levels, ii) collect the Data by applying the OPF at each operating point to specify the generation
rescheduling, iii) evaluate the minimum CCT as index for TSA each operating point following the
expected set of contingencies (self-clearance three-phase short circuit at a preselected set of critical
locations), iv) apply different feature selection algorithms (NCA, MRMR, and K-means algorithms) to
select the best correlated features with TSA indicator, v) build GPR predictive models based on the
selected features from three feature selection algorithms, and vi) evaluate the predictive models using
performance indices to enhance the accuracy.
2.1. Transient stability assessment
TSA is influenced by the initial operating states as well as the severity of applied disturbance. The
most sever contingency is the self-cleared three-phase short circuit which is used in this study [16]. The
dynamic response of generators depends on the fault duration and location. The synchronization among
generators is governed by the swings between generator rotor angles where the angular deviation between
generators should not exceed the predefined accepted limit to consider the system stable. The CCT represents
the minimum fault duration where the system remains stable without loss of synchronization following the
clearance of fault. The first generator starts to out of step is called critical generator. The CCT is specified by
the system operator according to the settings of protection system and dynamic behavior of generators which
usually 150 or 200 milliseconds [17]. The fault duration beyond this limit makes the system loss of the ability
3. Int J Elec & Comp Eng ISSN: 2088-8708
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4651
to preserve the system stability. The CCT can be calculated using TDS by increasing the fault duration till
one of the generators out of synchronization. TDS solves the power system differential and algebraic
equations using step by step calculations during pre-fault, during fault, and post-fault to simulate the system
dynamics. If the applied fault makes the rotor angle deviation reaching this limit, the duration of the applied
fault is called CCT which depends on the net kinetic energy of all generators and the produced
electromechanical oscillations [16]. Fast online TSA tool enable the system operator to activate the required
countermeasures to force the system to stabile region. The application of fast TSA tools such as GPR
predictive model is significantly reducing the required computation time as well as the burden of data
collection. The minimum CCT was considered as 0.15 second in this study according to the commission
regulation (EU) 2016/631 of 14 April 2016 which was established to specify the network code requirements
for grid connection of generators [17]. Therefore, every generator should have CCT longer than the specified
operating time limit of circuit breaker to avoid out of synchronism. DigSilent software is used to simulate the
test system and perform the necessary calculations.
2.2. Data collection
The analytical investigation of online TSA was conducted using 66-bus test system in Figure 1. The
system consists of 16-machine, 54-transmission line and 42 constant impedance loads [17]. The system is
divided into three areas (A, B and C) connected through three double circuit tie lines. The system was
developed to investigate several stability problems based on the relevant characteristic parameters of
European power system. The test system was modifying by adding four RES stations at the tie lines
connecting different areas. The original test system was designed to supply 16.516 gigavolt-ampere (GVA)
total demand with power exchange of 1000 MVA from area A to each of area B and area C. RES stations
were installed with 200 MVA wind system and 50 MVA PV system at the terminals of the tie lines
connecting different areas.
Variety of operating points were collected by random varying of loads and applying of OPF within
acceptable limits [17]. At each operating point, CCT was calculated using the electromechanical transient’s
evaluation. Table 1 presents the offline collected variables using OPF. Figure 2 shows the classification of
system states with fault duration less than 0.5 second. Accordingly, if fault duration less than 150 millisecond
leads to loss of synchronism, the system was considered as transiently unstable. The collected data was
divided randomly into training set of 600 operating points and testing set of 150 operating points.
Figure 1. Single line diagram of 66-bus test system
2.3. PV system modelling
The PV array consists of many modules which are connected in series and parallel according to the
desired power. The PV module consists of solar cells, the capacitor at direct current (DC) bus for voltage
control, power electronic devices, integrated controller, and energy storage system. Figure 3 presents the
block diagram of the PV composite model as described in DigSilent software [18].
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Table 1. Offline collected variables using OPF
Variable Name No. Variable Name No.
PQ-Area Area active and reactive power 6 Tap-T Transformer tap changer setting 28
PQ-G Generator active and reactive power 32 PQ-Load Load active and reactive power 84
PQ-Line Lines active and reactive power 108 PQ-RES Active and reactive power of RES 6
V-Buss Bus voltage 132 Total number of variables 396
Figure 2. Classification of operating points stability using CCT
Figure 3. Composed model for PV system in DigSILENT
Solar cell is represented by an ideal single-diode module. The output current is defined as in (1):
𝐼 = 𝐼pv − 𝐼o [𝑒𝑥𝑝(
𝑞(𝑉+𝐼𝑅s)
𝑘 𝑇 𝑎
)
− 1] −
𝑉+𝐼𝑅s
𝑅p
(1)
where 𝐼pv and 𝐼o are photogenerated and saturation currents, V is terminal voltage, 𝑅P and 𝑅s are parallel and
series resistances, 𝑞 is electron charge, 𝑇 is temperature, and 𝑎 is the diode’s ideality factor.
The output voltage is connected across DC-bus capacitance. the capacitor protects for the PV array
during abnormal conditions providing isolation between the PV array and the grid. The charging and
discharging process enables the capacitor to operate as energy storage device. This improves the ability of
maximum power point tracking (MPPT) and active power control to inject the schedule output power from
the PV system to the grid. The approximating linear prediction algorithm is used to evaluate the DC- link
voltage for achieving MPPT operation [19]. The accomplished variation in dc-link voltage and control signal
of frequency stability are used to control the d-component of reference current through PI-controller. The PI-
controller is used to regulate the DC voltage across the capacitor terminals by comparing to the PV array
reference voltage and the voltage across the capacitor terminals. The output of the PI controller is evaluated
based on deviation of the array output voltage from the required DC voltage across the capacitor. Additional
input signal can be added to compensate the fluctuation of grid frequency from the reference value. The
output power injected to the grid through static generator which simulates the inverter behavior generating
the AC signal based its input d-q components of reference current controlled signals (id,ref, iq,ref). The static
generator represents a current source model with output current (Ig) as in (2) at the grid voltage (Vg=Vr+jVi)
and frequency. They are synchronized with grid using the d-q reference angle.
𝐼g = (𝑣r ∗ 𝑖d,ref |𝑉
g|
⁄ − 𝑣i ∗ 𝑖q,ref |𝑉
g|
⁄ ) + 𝑗(𝑣i ∗ 𝑖d,ref |𝑉
g|
⁄ + 𝑣r ∗ 𝑖q,ref |𝑉
g|
⁄ ) (2)
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2.4. Wind system modelling
The doubly-feed induction generator (DFIG) is implemented within the DigSilent as static
generator. Figure 4 presents the main parts of 6 MVA, 0.69 kV wind system which can be explained as
follows: i) DFIG generates the power based on the input mechanical power and the controlled rotor voltage;
ii) the rotor model includes the aerodynamic model of turbine, shaft, and pitch angle control models. The
output mechanical power to DFIG depends on the wind speed and the specified value of reference speed;
iii) compensation block calculates the rotor voltage based on the calculated rotor input current signal from
current controller and output active and reactive power from DFIG. The current controller controls the output
power and limits of rotor current and frequency deviation; iv) PQ control model evaluates the active and
reactive reference currents for rotor side converter (RSC) to control the variation on rotor voltage according
to the target output active and reactive power. The inputs are the terminal voltage, reference speed,
over-frequency control signals and under-frequency control signals; and v) rotor protection inserts crow-bar
circuit during faults and under-frequency controller. Current, voltage and frequency measurement devices are
used for transformation into stator voltage-oriented reference frame.
Figure 4. Block diagram of 6 MW wind turbine model in DigSilent
2.5. TSA based GPR protective model
The GPR predictive model was implemented to relate the selected features and the CCT for TSA.
GPR is accurate prediction algorithm which was used for measuring the goodness of the selected features
with the associated response in many applications [20]. GPR is used to predict the CCT using the selected
features. GPR is a supervised learning algorithm which classifies the response using covariance relationship
to represent the similarity of predictor. The fitrgp function is used to fit the GPR model in MATLAB
software and is used in this study. Various standard kernel functions can be used to represent the effect of the
response one point, 𝑥𝑖 by other one, 𝑥𝒋. The default one in the fitrgp function is the squared exponential
kernel function. The selection of the best kernel function was obtained iteratively to improve the prediction
accuracy based on the correlation between selected features and the CCT. The accuracy of correct system
state of stability prediction was measured using the relation between the number of correct assessments of
system stability to the total number of operating points. The correct assessment was considered when the
absolute deviation between predicted and calculated CCT less than 5 milliseconds. The obtained best results
were obtained using the rational quadratic kernel function in (3). The collected 750 data sets were divided
into a training set 𝑇1 = {(𝑥i, 𝑦i)|𝑖 = 1,2, 3, ⋯ 𝑀1}, and testing set 𝑇2 = {(𝑥i, 𝑦𝑖)|𝑖 = 1,2, 3, ⋯ 𝑀2}.
𝑘(𝑥i, 𝑥j ↓ 𝜃) = σf
2
(1 + (𝑥i − 𝑥j)
T
(𝑥i − 𝑥j) 2α𝜎l
2
⁄ )
−α
(3)
Where, α is a positive scale-mixture parameter, 𝜎l is the characteristic length scale and 𝜎f.is standard
deviation.
Many methods used to reduce the number of variables by selecting the best correlated ones with
TSA. Applying different method may determine different promising features from one algorithm to another
as well as using the actual values rather than using normalized values [21], [22]. The performance evaluation
of the GPR prediction model was performed in terms of indexes as presented in (4) to (6). The indexes
include the accuracy of true classifying the system state into stable and unstable (Ntrue/N) with error less than
5 milliseconds in CCT prediction, the mean absolute error (MAE), root mean square error (RMSE), and the
goodness of the regression based on the ratio of variation (0<=R2
<=1). The closest 𝑅2
to one is an indication
to the healthy regression process.
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𝑀𝐴𝐸 =
1
𝑁
∑ |
𝑦k
̅̅̅̅−𝑦k
𝑦k
̅̅̅̅
|
𝑁
𝑘=1 (4)
𝑅𝑀𝑆𝐸 = √∑ (𝑦k
̅̅̅ − 𝑦k)2
𝑁
𝑘=1 𝑁
⁄ (5)
𝑅2
= 1 −
𝑅𝑀𝑆𝐸
𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒(𝑦
̅)2 (6)
where 𝑦k
̅̅̅ is the predicted CCT value using GPR model and 𝑦𝑘 is the calculated value.
3. RESULTS AND DISCUSSION
In this section, NCA, MRMR, and K-means clustering algorithms are used for data mining and
features selection. Furthermore, GPR was used to build the corresponding three predictive model to select the
best one. The results are compared with the TSA based on TDS results.
3.1. GPR based neighborhood component analysis features selection
The neighborhood component analysis (NCA) is a filter type feature selection algorithm which
depends on the features’ similarities and correlations [23]. NCA is considered as a robust feature selection
technique which can be applied for features ranking and selection. The method works on the diagonal
adaptation of NCA with regularization to minimize a loss function such as RMSE or MAE. The
regularization term tends to reduce the weights of the irrelevant features to zero [24], [25]. Figure 5(a)
presents the fitted values of CCT using the NCA algorithm relative to the actual values of CCT which
explains the ability of NCA algorithm to predict the response values and presents fitted CCT and the actual
values. The mean squared error as the measure of accuracy of the predict relative to the actual values of CCT
is 0.005. Figure 5(b) presents the features weight where the small correlation features with the CCT have
nearly zero weights.
The features with high weight factors were collected to be used in predictive model implementation.
The selected 30 features which have high correlations with the CCT are presented in Table 2. The data sets of
the selected 30 features and corresponding actual CCT were used to build GPR predictive model.
(a) (b)
Figure 5. The fitted values of (a) CCT and (b) the features weight using NCA
Table 2. The selected 30 features using NCA algorithm.
Variable High score 30 features by NCA No
1 PQ-Area Sum PA-Sum QA-Sum QB-Sum PC-Sum QC 5
2 PQ-G Pg1- Pg2 - Pg3 -Qg4- Pg6-Qg8 -Pg8-Qg9-Pg10-Pg1- Pg12-Pg13 12
4 PQ-Line PA1-4/QA1-4/PA1-2/QA1-2/PA4a-5/PA2-5a/PA2-B5b/QB1-2/PB2-3/QB2-3/PB5-9/PB7-8/PC1-2 13
Total number of selected features 30
The performance evaluation of GPR is presented in Table 3. The results show the goodness of the
regression process during the training process to classify the system states into stable or unstable correctly
where the error between the predicted CCT relative to the calculated CCT is almost less than 2 milliseconds
and standard deviation of 0.34 milliseconds as shown in Figure 6(a). Figure 6(b) presents the CCT obtained
during the testing stage for 50 out of 150 unforeseen operating points. The GPR protective model was able to
7. Int J Elec & Comp Eng ISSN: 2088-8708
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classify the system state of 128 out of 150 operating point correctly. The results indicate the difficulty to
exact mapping of system dynamics by the selected features at some points. The maximum error is 30
milliseconds with standard deviation of 6 millisecond. This is due to the sensitivity of the CCT for the
variation in operating conditions. However, the GPR model classifies the system states into stable or unstable
correctly where most relative values are in the same side from the border line of 150 millisecond.
Table 3. The performance indexes for GPR model based NCA evaluation
Data %Acc RMSE R2 MAE
Training 100 0.0003 0.999 0.0011
Testing 85 0.0172 0.877 0.0126
All Data 96.3 0.0077 0.973 0.0113
(a) (b)
Figure 6. The error between calculated and predicted CCT using NCA during (a) training and (b) testing
3.2. GPR based minimum redundancy maximization algorithm features selection
The minimum redundancy maximum relevance (MRMR) algorithm is used to rank the features
according to their importance with respect to the target response. The basic idea of MRMR depends on
maximizing the mutual information which relate the different discrete variables. MRMR considers the mutual
information between variables and the target classes of response. The mutual information represents the
independence of any two variables and is defined as in (7) [26], [27];
𝐼(𝑋; 𝑍) = ∑ ∑ 𝑝(𝑥, 𝑧) log (
𝑝(𝑥,𝑧)
𝑝(𝑥)𝑝(𝑧)
)
𝑥∈𝑋
𝑧∈𝑋 (7)
where 𝑝(𝑥, 𝑧) represents the joint probability distribution function (PDF) of x and z. 𝑝(𝑥) and 𝑝(𝑧) are the
marginal PDF of x and z respectively.
MRMR algorithm aims to maximize the relevance (𝑉
𝑥) between selected feature (𝑥𝑖) and the target
class C. The relevance value is defined as in (8).
𝑉
x = 𝐼(𝑥i; 𝐶) (8)
Maximizing the redundancy (Mutual distance between variables, 𝑊
x) of variable 𝑥𝑖 with respect to the set of
S variables enhances the classification or regression process. The redundancy is defined in (9).
𝑊
𝑥 =
1
𝑆
∑ 𝐼(𝑥i, 𝑥j)
𝑥i,𝑥j∈𝑆 (9)
The MRMR algorithm ranks the features using the mutual information quotient (MIQ) value which involves
relevance maximizing and redundancy minimizing simultaneously as in (10).
𝑀𝑎𝑥𝑥∈𝑆𝑀𝐼𝑄𝑥 = 𝑀𝑎𝑥𝑥∈𝑆 (
𝑉x
𝑊x
) (10)
Figure 7 shows the features ranking based on their importance with respected to the CCT. The 30
features with higher score are selected and tabulated in Table 4. The ranking of the selected features shows
the variation among the NCA and MRMR algorithms in features selection. The high ranked 30 features using
MRMR algorithm were selected to build GPR predictive model.
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Figure 7. The score of features using MRMR algorithm
Table 4. The selected 30 features using MRMR algorithm
Variable Higher weight 30 features by MRMR No
1 Sum PQ-Area Sum PA-Sum QB- 2
2 PQ-G Qg5-Qg6-Pg13 3
3 V-Bus VmA3 -VmA6-VmA7-VmB1-VaB8-VmC6-VmC12 7
4 PQ-Line
QA1-4/QA1-2/PA4a-5/PA2-5a/QA5a-5b/PA6-7/QB1-2/QB2-5/QB6-C10/QB3-11/PC1-2/QC2-
3/QC3-6/PC5-6/QC5-16/PC7-8/QC7-8/PC9-10/QC11-12/PC13-14
18
Total number of selected features 30
Figure 8(a) presents the error in CCT prediction using GPR predictive model during training
process. The model was able to predict the system state of all training data sets with error less than
2 milliseconds and standard deviation of 0.27 milliseconds. Figure 8(b) presents the CCT obtained during the
testing stage for 50 out of 150 unforeseen operating points. The GPR protective model was able to classify
the system state of 120 out of 150 operating point correctly. The results show that the GPR predictive model
based NCA features selection algorithm was slightly accurate than the GPR model based MRMR selected
features. The maximum error is 30 milliseconds at five operating points with standard deviation of
7.8 milliseconds. The variation is due to the strategy of each algorithm to pike up the preferred feature from
each correlated group. Also, the reduction of the accuracy occurs due to the nonzero weights corresponding
to the other features. Only six features were common between NCA and MRMR model. For more
investigation, third feature selection-based K-means algorithm was used to classify the features into
30 clusters. The performance indexes of the GRP predictive model based MRMR is tabulated in Table 5.
(a) (b)
Figure 8. The error between calculated and predicted CCT using MRMR during (a) training and (b) testing
3.3. GPR based K-means clustering algorithm
K-means clustering algorithm is recursive, sequential, and heuristic search algorithms that add
or/and remove features using selection criterion into subsets of variables [26]. K-means algorithm depends on
the variable’s allocation into an arbitrary number of clusters based on the minimization of the average
squared Euclidean distance between the centroid of the cluster and its observation. The allocation process is
repeated iteratively to positioning the variables closed to the k centroids to separate variables into k clusters
(groups). In this study, K-means algorithm was used to categorize the collected variables based on the
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Risk assessment of power system transient instability incorporating … (Ayman Hoballah)
4657
observations to 30 clusters containing the nearest variables to the centroids. The variables in each group have
similar characteristics which keep the Euclidean distance to the corresponding centroid minimal. The
separation depends on the actual data instead of the dissimilarity between each two variables. Therefore, the
closest variables to the centroids are selected to represent the groups. The shortage of K-means clustering
algorithm in features selection is that the clusters have the same distance from the centroids are treated
equally and the selected best ones depends on the method of ranking not the correlation with CCT. Table 6
presents the selected 30 features using K-means algorithms. The selected 30 features were used to build GPR
predictive model. The results show that there are 15 features are common between NCA and K-means
features selection algorithms where only 6 features are common between MRMR and K-means features
selection algorithms. Figure 9(a) shows the error in CCT for the GPR predictive model during training
process. The maximum error of GPR model was 1.4 milliseconds with standard deviation of
0.3 milliseconds. Figure 9(b) presents the predicted CCT of unforeseen 50 operating points out of 150 test
operating points using GPR model relative to the calculated CCT using TDS during the testing stages. The
GPR model was able to predict 100 out of 150 operating points. The performance indices of the model for
training, testing and all data sets are tabulated in Table 7. The performance indices are less quality than the
performance of the GPR models based NCA and MRMR features selections.
Table 5. The performance indexes for GPR model based MRMR evaluation
Data %Acc RMSE R2 MAE
Training 100 0.0003 1.000 0.001
Testing 79.3 0.0176 0.871 0.1126
All Data 95.8 0.0079 0.971 0.0233
Table 6. The selected 30 features using k-means algorithm
Variable Selected 30 features by K-means No.
1 Sum PQ-Area Sum PA-Sum PB-Sum QB-Sum Pc-Sum Qc 3
2 PQ-G Pg1 -Pg2- Pg3-Qg8 - Pg8 -Pg9-Pg12-Pg13-Qg13-Pg14-Pg15-Qg16 10
3 V-Bus VmA4-VmB11-VmC5-VmC13 4
PQ-Load P_L10-P_L24-P_L20 3
4 PQ-Line PA4a-5/PA2-5a/QB1-2/PB2-3/QC2-3/PC18-19 9
Total number of selected features 30
(a) (b)
Figure 9. The error between calculated and predicted CCT using K-means during (a) training and (b) testing
Table 7. The performance indexes for GPR model-based k-means evaluation
Data %Acc RMSE R2 MAE
Training 99.7 0.0003 0.999 0.0011
Testing 66.34 0.0181 0.863 0.1398
All Data 92.2 0.0081 0.969 0.0288
3.4. Identification of hybrid features selection
To verify accuracy of the GPR predictive model, the results obtained from the three investigated
features selection algorithm are compared with the accurately obtained results from TDS. Figure 10 presents
the comparison between the CCT corresponding to randomly selected 20 operating points using TDS and the
GPR predictive model based the 30 selected features using NCA, MRMR and K-means algorithms. The
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results show the ability of GPR models to state the system stability. The investigation is investigated based
on the selection of best features as well as testing the GPR predictive model using test operating points.
Figure 10. The CCT using TDS and GPR predictive models for 20 unforeseen operating points
The results from the three algorithms show that 6 features are common as presented in
Tables 3, 5, and 7. The selected features depend on the used method of algorithms during ranking and
correlation process. In this step, GPR predictive model was built using the common and best features from
the three methods. The features with higher scores using NSA and higher weights using MRMR were
collected sequentially and used to build new hybrid GPR models. The accuracy is enhanced from 83, 79.3
and 63.34 for NCA, MRMR and K-means respectively to 89.23% with 26 selected features during training
process. The selected features with high accuracy are tabulated in Table 8. Table 9 displays the performance
indices of the GPR model using the combined features from the three techniques where the number of the
selected features are 26. The results show the enhancement not only in the testing data but also in all data
sets. Therefore, the application of different feature selection algorithms can be used to discover hidden
characteristics and correlation within collected big data.
Table 8. The selected features with high accuracy of the hybrid GPR predictive model
Variable Common features Features (NCA) Features (MRMR) No.
1 PQ-Area Sum_PA -Sum QB Sum PC 3
2 PQ-G Pg13 Pg1-Pg2-Pg8-Qg8-Pg3-Pg12-Pg10 Qg5 9
3 PQ-Line P_A4aA5-P_A2A5a-Q_B1B2 P_C1C2-P_A1A2
P_A6A7-Q_C2C3-Q_C5C16-
Q_B2B5-Q_C11C12-P_C5C6
11
4 V-Buss VmC6-VmA7-VmA6 3
Total number of selected features 26
Table 9. The evaluation of the hybrid GPR model based combined selection
Training stage Testing stage All Data
% Acc RMSE R2 MAE % Acc RMSE R2 MAE % Acc RMSE R2 MAE
100 0.0011 0.999 0.0026 89.23 0.0053 0.985 0.013 0.971 0.0028 0.996 0.0053
4. CONCLUSION
This work presents transient stability assessment of power system using analytical methods-based
feature selection techniques. The effect of the RES was considered during data collection through random
variation of load levels and the penetration level of RES. Minimum CCT is considered as indicator for TSA
which represents the system dynamic stability following self-clearance three-phase faults at critical fault
locations. GPR model was built for online monitoring of the TSA using group of selected features which can
be collected using PMU units. The features were selected using NCA, MRMR and K-means algorithms. The
application of the different feature selection algorithms presents different correlations between the selected
features and CCT. The selection of the common features and the features with high correlations with CCT
from different feature selection algorithms enhances the performance of the GPR model. The results show the
high accuracy of the GPR predictive model (97.1%) to estimate CCT for TSA over a wide range on operating
points. The proposed method can be used to build GPR predictive model for TSA in large scale power
systems.
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Risk assessment of power system transient instability incorporating … (Ayman Hoballah)
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ACKNOWLEDGEMENTS
This research was funded by Deanship of Scientific Research, Taif University, grant no. 1-441-99.
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BIOGRAPHIES OF AUTHORS
Ayman Hoballah received the B.Sc. and M.Sc. degrees in Electrical Engineering
from Tanta University, Egypt in 1996 and 2003. Since 1998, he has been with the Electrical
Power and Machines department, Faculty of Engineering, University of Tanta/Egypt. He
completed his Ph.D. in electrical engineering department from the university Duisburg-Essen,
Germany in 2011. His Ph.D. thesis focuses on the power system dynamic stability.
Enhancement utilizing artificial intelligent techniques. His current reach interests include power
system stability, DGs, smart grid, grounding systems and optimization techniques. He is an ass.
professor at Taif University. Email: ayman.h@tu.edu.sa.
Salah K. Elsayed was born in 1982 He obtained his B.Sc. and M.Sc. and PhD
degrees from the Electrical Engineering Department, Faculty of Engineering- AL-Azhar
University, Cairo, Egypt in 2005, 2009, and 2012 respectively. From 2012 to 2017, he was a
lecturer with the Electrical Engineering Department AL-Azhar University, then he promoted to
an associate professor, in 2017. He joined the Electrical Engineering Department, Faculty of
Engineering, Taif University, KSA. His research interests include power systems analysis and
operation, Power Systems Stability and control, power system optimization techniques,
Artificial Intelligence Systems Applications in Power Systems and renewable energy sources.
Email: sabdelhamid@tu.edu.sa
Sattam Al Otaibi Department of Electrical Engineering, Taif University, Ta’if,
Saudi Arabia. Sattam Al Otaibi was the Head of the public relation Center, Taif University,
Saudi Arabia. He is a researcher and an academician specializing in electrical engineering and
nanotechnology. His practical experience in the field of industry, education, and scientific
research has been formed through his research work and through his mobility among many
companies, institutions, and universities as well as active participation in research centers that
resulted in much scientific research published in refereed scientific bodies. Email:
srotaibi@tu.edu.sa
Essam Hendawi was born in Egypt, 1968. He received his B.Sc., M.Sc., and Ph.D.
degrees in Electrical Power and Machines Engineering from the Faculty of Engineering, Cairo
University, Egypt, in 1992, 1998, and 2009 respectively. He is a Researcher in the Electronics
Research Institute (ERI), Egypt, and he is currently working as an ass. Professor in the Electrical
Engineering Department, College of Engineering, Taif University, Saudi Arabia. His research
interests include electrical machine drives, converters, microcontrollers, and renewable energy.
Email: essam@tu.edu.sa
Nagy I. Elkalashy received the B.Sc. and M.Sc. degrees from the Electrical
Engineering Department, Faculty of Engineering, Menoufia University, Shebin Elkom, Egypt,
in 1997 and 2002, respectively. He received the Doctoral of Science in Technology (DSc. with
Distinction) in Dec. 2007 from Helsinki University of Technology (TKK), Otaniemi, Finland.
However, he was with Taif University, Taif, Saudi Arabia from 2015 to 2020. Currently, he is
the head of the Electrical Engineering Department, Faculty of Engineering, Menoufia
University. His research interests include protection, fault location determination, smart grids,
system transients, HV engineering, switchgear technology, digital signal processing for power
system applications. Email: n.elkalashy@tu.edu.sa
Yasser Ahmed obtained his B.Sc. from Tanta University, Egypt, Faculty of
Engineering, Electrical Power and Machines department in 1999. He received M.Sc. and Ph.D.
from Cairo University, Egypt, Faculty of Engineering in 2006 and 2014, respectively. He works
at Electronic Research Institute (ERI), Egypt, Power Electronics and Energy Conversion
department since 2001. Currently, he is working at Electrical Engineering Department, College
of Engineering, Taif University, Saudi Arabia. His major interests are electric drives, electric
and hybrid electric vehicles, and modeling and simulation of electrical systems. Email:
y.abdelsalam@tu.edu.sa.