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.
IRJET- Static Analysis of a RC Framed 20 Storey Structure using ETABSIRJET Journal
This document summarizes a study that compares the performance of a fuzzy logic-based power system stabilizer (FLPSS) to other stabilizer techniques for damping oscillations in a single machine infinite bus power system model. The FLPSS uses fuzzy logic control with 36 rules to determine the stabilizing signal injected into the voltage regulator. Simulation results show the FLPSS reduces oscillations in angular position and speed faster than stabilizers using artificial neural networks, genetic algorithms, or particle swarm optimization. The FLPSS provides better damping over a range of operating points without complex mathematical modeling, demonstrating its effectiveness for enhancing power system stability.
Low Frequency Oscillations Damping by UPFC with GAPOD and GADC-voltage regulatorIOSR Journals
This document describes a new controller for a Unified Power Flow Controller (UPFC) to damp low frequency oscillations in power systems. The proposed controller is a Multi-Stage Fuzzy (MSF) PID controller with a genetic algorithm-based DC voltage regulator. The MSF controller uses multiple fuzzy logic controllers and a fuzzy switch to better handle system uncertainties. Membership functions for the fuzzy controllers are optimized using a genetic algorithm. Testing on a single machine infinite bus power system model shows the proposed controller more effectively damps low frequency oscillations under different operating conditions compared to other UPFC controllers. The design and implementation of the proposed controller is described to be simpler than other advanced control methods while still providing robust damping performance.
Design of Load Frequency Controllers for Interconnected Power Systems with Su...IOSR Journals
This document describes research applying the Bat Algorithm to optimize load frequency controller parameters for an interconnected two-area power system model with Superconducting Magnetic Energy Storage (SMES) units. The Bat Algorithm, inspired by bat echolocation behavior, is used to determine proportional-integral controller parameters that minimize frequency and tie-line deviations following load disturbances. Simulation results confirm the Bat Algorithm effectively damps oscillations, achieving fast stabilization to steady state values with SMES units providing favorable damping effects.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STABILIZER ON A SINGLE MACHINE ...EEIJ journal
In this paper the use of artificial neural network in power system stability is studied. A predictive
controller based on two neural networks is designed and tested on a single machine infinite bus system
which is used to replace conventional power system stabilizers. They have been used for decades in
power system to dampen small amplitude low frequency oscillation in power systems. The increases in
size and complexity of power systems have cast a shadow on efficiency of conventional method. New
control strategies have been proposed in many researches. Artificial Neural Networks have been studied
in many publications but lack of assurance of their functionality has hindered the practical usage of them
in utilities. The proposed control structure is modelled using a novel data exchange established between
MATLAB and DIgSILENT power factory. The result of simulation proves the efficiency of the proposed
structure.
ENHANCING RELIABILITY BY RECONFIGURATION OF POWER DISTRIBUTION SYSTEMS CONSID...Suganthi Thangaraj
The paper describes an effective method to reconfigure a power distribution system using optimization techniques. Here genetic algorithm is used for the reconfiguration to enhance reliability and to reduce losses. The reliability at the load points is evaluated using probabilistic reliability approach. For finding minimal cut sets and losses different algorithms are used. To maximise the reliability and to reduce the losses, the status of the switch is controlled using genetic algorithm. The effectiveness of the system is tested in 33 bus distribution system.
IRJET- Fuzzy Control Scheme for Damping of Oscillations in Multi Machine Powe...IRJET Journal
This document presents a fuzzy logic control scheme to damp oscillations in a multi-machine power system model using Unified Power Flow Controllers (UPFCs). The model consists of three generators connected to a nine bus system with four loads. Two UPFCs are placed between certain buses to control power flow. Fuzzy logic controllers are designed for the UPFCs based on their input-output relationships. Simulation results in MATLAB/Simulink show that the fuzzy logic controlled UPFCs effectively damp low frequency oscillations caused by faults, improving system stability compared to the uncontrolled system.
The Enhanced Performance SVC for Transient Instability Oscillation DampingPower System Operation
The document describes enhancing the performance of static var compensators (SVCs) for damping transient instability oscillations in power systems. It presents a technique of adding a dynamic oscillation damper co-located with an SVC to provide both reactive power compensation and real power support. Simulation results show that a typical SVC improves voltage regulation but does not minimize instability, while the proposed SVC plus damper system greatly enhances performance by damping oscillations during and after a simulated fault event.
IRJET- Static Analysis of a RC Framed 20 Storey Structure using ETABSIRJET Journal
This document summarizes a study that compares the performance of a fuzzy logic-based power system stabilizer (FLPSS) to other stabilizer techniques for damping oscillations in a single machine infinite bus power system model. The FLPSS uses fuzzy logic control with 36 rules to determine the stabilizing signal injected into the voltage regulator. Simulation results show the FLPSS reduces oscillations in angular position and speed faster than stabilizers using artificial neural networks, genetic algorithms, or particle swarm optimization. The FLPSS provides better damping over a range of operating points without complex mathematical modeling, demonstrating its effectiveness for enhancing power system stability.
Low Frequency Oscillations Damping by UPFC with GAPOD and GADC-voltage regulatorIOSR Journals
This document describes a new controller for a Unified Power Flow Controller (UPFC) to damp low frequency oscillations in power systems. The proposed controller is a Multi-Stage Fuzzy (MSF) PID controller with a genetic algorithm-based DC voltage regulator. The MSF controller uses multiple fuzzy logic controllers and a fuzzy switch to better handle system uncertainties. Membership functions for the fuzzy controllers are optimized using a genetic algorithm. Testing on a single machine infinite bus power system model shows the proposed controller more effectively damps low frequency oscillations under different operating conditions compared to other UPFC controllers. The design and implementation of the proposed controller is described to be simpler than other advanced control methods while still providing robust damping performance.
Design of Load Frequency Controllers for Interconnected Power Systems with Su...IOSR Journals
This document describes research applying the Bat Algorithm to optimize load frequency controller parameters for an interconnected two-area power system model with Superconducting Magnetic Energy Storage (SMES) units. The Bat Algorithm, inspired by bat echolocation behavior, is used to determine proportional-integral controller parameters that minimize frequency and tie-line deviations following load disturbances. Simulation results confirm the Bat Algorithm effectively damps oscillations, achieving fast stabilization to steady state values with SMES units providing favorable damping effects.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STABILIZER ON A SINGLE MACHINE ...EEIJ journal
In this paper the use of artificial neural network in power system stability is studied. A predictive
controller based on two neural networks is designed and tested on a single machine infinite bus system
which is used to replace conventional power system stabilizers. They have been used for decades in
power system to dampen small amplitude low frequency oscillation in power systems. The increases in
size and complexity of power systems have cast a shadow on efficiency of conventional method. New
control strategies have been proposed in many researches. Artificial Neural Networks have been studied
in many publications but lack of assurance of their functionality has hindered the practical usage of them
in utilities. The proposed control structure is modelled using a novel data exchange established between
MATLAB and DIgSILENT power factory. The result of simulation proves the efficiency of the proposed
structure.
ENHANCING RELIABILITY BY RECONFIGURATION OF POWER DISTRIBUTION SYSTEMS CONSID...Suganthi Thangaraj
The paper describes an effective method to reconfigure a power distribution system using optimization techniques. Here genetic algorithm is used for the reconfiguration to enhance reliability and to reduce losses. The reliability at the load points is evaluated using probabilistic reliability approach. For finding minimal cut sets and losses different algorithms are used. To maximise the reliability and to reduce the losses, the status of the switch is controlled using genetic algorithm. The effectiveness of the system is tested in 33 bus distribution system.
IRJET- Fuzzy Control Scheme for Damping of Oscillations in Multi Machine Powe...IRJET Journal
This document presents a fuzzy logic control scheme to damp oscillations in a multi-machine power system model using Unified Power Flow Controllers (UPFCs). The model consists of three generators connected to a nine bus system with four loads. Two UPFCs are placed between certain buses to control power flow. Fuzzy logic controllers are designed for the UPFCs based on their input-output relationships. Simulation results in MATLAB/Simulink show that the fuzzy logic controlled UPFCs effectively damp low frequency oscillations caused by faults, improving system stability compared to the uncontrolled system.
The Enhanced Performance SVC for Transient Instability Oscillation DampingPower System Operation
The document describes enhancing the performance of static var compensators (SVCs) for damping transient instability oscillations in power systems. It presents a technique of adding a dynamic oscillation damper co-located with an SVC to provide both reactive power compensation and real power support. Simulation results show that a typical SVC improves voltage regulation but does not minimize instability, while the proposed SVC plus damper system greatly enhances performance by damping oscillations during and after a simulated fault event.
This document discusses several applications of fuzzy logic in electrical systems, including induction motor control, switched reluctance motor control, excitation control in automatic voltage regulators, and fuzzy logic control in an 18 bus power system. It focuses on using fuzzy logic for automatic voltage regulation, describing the typical components of a power system, challenges with conventional controllers, and presenting simulation results that demonstrate how a fuzzy logic controller can effectively regulate the voltage of a synchronous generator.
1. The document describes a hybrid wind and photovoltaic (PV) system connected to the main grid. It proposes using an adaptive neuro-fuzzy inference system (ANFIS) for maximum power point tracking (MPPT) of the PV array and a fuzzy logic controller for pitch angle control of the wind turbine.
2. Simulation results show that the ANFIS MPPT controller can track the maximum power point with less fluctuation and faster convergence compared to traditional MPPT methods. The fuzzy logic pitch controller provides faster response for high wind speeds, improving dynamic performance and preventing damage.
3. The complete hybrid system model is implemented in Matlab/Simulink, including the PV and wind systems, power converters
Differential game approach for the analysis of two area load frequency controlIRJET Journal
This document discusses using a differential game approach for analyzing load frequency control in a two-area power system. It first provides background on load frequency control and describes the traditional proportional-integral controller approach. It then introduces differential games as a tool for solving multi-objective optimization problems and proposes applying a cooperative differential game to control the two areas. The game would derive non-cooperative and cooperative equilibrium solutions to maintain system frequency stability and balance generation and loads across areas. Simulation results would compare this approach to traditional control methods.
Energy Balance in AC Is-landed Micro-grid by Frequency Bus Signaling MethodIRJET Journal
This document discusses energy balance techniques for microgrids with distributed energy resources like renewable energy sources and energy storage systems. It proposes a frequency bus signaling method to achieve energy balance without centralized communication. The method uses local frequency measurements to signal distributed power sources about how much power to generate in order to maintain the state of charge of energy storage below its maximum limit. This autonomous control strategy relies only on local signals and measurements to coordinate power generation and balance supply and demand within the microgrid.
The aim of this paper is to prove that fuzzy logic algorithm is a suitable control technique for fast processes such as electrical machines. This theory has been experimented on different kinds of electrical machines such as stepping motors, dc motors and induction machines (with 6 phases) and the experimental results show that the proposed fuzzy logic algorithm is the most suitable control technique for electrical machines since this algorithm is not time consuming and it is also robust between plant parameters variations.
This document proposes techniques to enhance the dynamic responses of a microgrid system containing photovoltaic and wind power generation. For photovoltaic power, an artificial neural network trained with genetic algorithm is used for maximum power point tracking, which can track the peak power point with high accuracy. For high wind speeds, a fuzzy logic controller is used for pitch angle control of the turbine, providing faster responses to reduce power fluctuations. The models are developed in Matlab/Simulink to analyze the microgrid system performance under different operating conditions.
The power supplied by photovoltaic DC–DC converter is affected by two factors, sun irradiance and temperature. Therefore, to improve the performance of the PV system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique present some difficulties in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). In this paper, we present a comparative study of the PV standalone system which is controlled by three techniques. The first one is conventional based on the observation and perturbation technique, the other are intelligent based on fuzzy logic according Mamdani and Takagi-Sugeno models. The investigations show that the fuzzy logic controllers provide the best results and Takagi-Sugeno model presents the lower overshoot value.
An IntelligentMPPT Method For PV Systems Operating Under Real Environmental C...theijes
The sun irradiance (G) and temperature (T) are the two main factors that affect the output power gained from the photovoltaic (PV) DC–DC converter. Therefore, to enhance the performance of the overall system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique, experience difficulty in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). The selection of the membership functions (MFs) and the fuzzy sets (FSs) numbers are crucial in the performance of the FLC based MPPT. Accordingly, this work presents numerous adaptive neuro-fuzzy systems to automatically adjustthe fuzzy logic controller membership functions as an alternative to the trial and error approach, which waste time and effort in MPPT design. For this purpose an adaptive neuro-fuzzy system is developed in MATLAB/Simulink to determine suitable MFs and the FSs for the fuzzy logic controller. The effects of different types of MFs and the FSs are deeply investigated using real data collected from the rooftop PV system. The investigations show that the fuzzy logic controller with a triangular membership function and seven fuzzy setsprovides the best results
Average dynamical frequency behaviour for multi-area islanded micro-grid netw...TELKOMNIKA JOURNAL
A micro-grid is a part of power system which able to operates in grid or islanding mode. The most important variable that able to give us information about the stability in islanded micro-grid network is the frequency dynamical responses. The frequency analysis for multi-area micro-grid network model may involve a complicated of mathematical equations. This makes the researcher intending to omit several unnecessary parameters in order to simplify the equations. The purpose of this paper is to show an approach to derive the mathematical equations to represent the average behavior of frequency dynamical responses for two different micro-grid areas. Both of networks are assumed to have non-identical distributed generator behavior with different parameters. The prime mover and speed governor systems are augmented with the general swing equation. The tie line model and the information of rotor angle was considered. Then, in the last section, the comparison between this technique with the conventional approach using centre of inertia (COI) technique was defined.
Improved dynamic performances of multi area reheat thermal agc power systems ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A review on SVC control for power system stability with and without auxiliary...journalBEEI
Since the beginning of the last century, power system stability has been recognized as a vital problem in securing system operation. Power system instability has caused many major blackouts. This paper reviewed the previous technical works consisting of various methods of optimization in controlling power system stability. The techniques presented were compared to optimize the control variables for optimization of power system stability. Power system stability enhancement has been investigated widely in literature using different ways. This paper is focusing on SVC performance for enhancing power system stability either through SVC controlled itself or SVC controlled externally by other controllers. Static VAR compensators (SVCs) are used primarily in power system for voltage control as either an end in itself or a means of achieving other objectives, such as system stabilization.The analysis on performance of the previous work such as advantages and findings of a robust method approach in each technique was included in this paper.
Coordinated Control of Interconnected Microgrid and Energy Storage System IJECEIAES
Several microgrids can be interconnected together to enhance the grid reliability and reduce the cost of supplying power to an island area where conventional power grid cannot be connected. Source and load demand do not properly balance always. Besides that, sometimes power and frequency fluctuation has occurred in MG at island mode. Need to design a special control for maintaining the state of charge (SoC) of energy storage system. This paper proposes a new power supply system for an island area that interconnects two microgrids with a single energy storage system (ESS). An algorithm has been proposed that control the microgrids energy storage system for spinning reserve and load power/frequency regulation purpose. The minimum loading constraints of diesel engine generator (DEG) is considered and the SOC of the ESS is properly maintained.
This present paper includes the study Load Frequency Control (LFC) of power systems with several nonlinearities
like Generation Rate Constraint(GRC) and Boiler Dynamics (BD) including Superconducting
Magnetic Energy Storage (SMES) units using Type-2 Fuzzy System (T2FS) controllers . Here, Load
frequency control problem is dealt with a three – area interconnected system of Thermal-Thermal-Hydal
power system by observing the effects and variations of dynamic responses employing conventional
controller, Type-1 fuzzy controller and T2FS controller considering incremental increase of step
pertubations by 10% in the load. The salient advantage of this controller is its high insensitivity to large
load changes and plant parameter variations even in the presence of non-linearities. As the non-linearities
were considered in the system, the conventional and classical Fuzzy controllers does not provide adequate
control performance with the consideration of above nonlinearities. To overcome this drawback T2FS
Controller has been employed in the system. Therefore, the efficacy of the proposed T2FS controller is
found to be better than that of conventional controller and Type-1 Fuzzy controller in cosidreration with
overshoot, settling time and robustness.
Coordination of Adaptive Neuro Fuzzy Inference System (ANFIS) and Type-2 Fuzz...IJECEIAES
This document summarizes a research paper that coordinates an adaptive neuro fuzzy inference system (ANFIS) and type-2 fuzzy logic system (T2FLS) power system stabilizer (PSS) to improve the stability of a large-scale power system. The ANFIS parameters are obtained through an offline training process, while the T2FLS parameters are determined based on knowledge gained from the ANFIS parameters. Simulation results show that the T2FLS-PSS is able to maintain stability better than conventional/ANFIS-PSS by decreasing peak overshoot and settling time for rotor speed and angle during local and inter-area oscillations. The T2FLS-PSS also performs better than other PSS types
This document summarizes a research paper that proposes a network-based coordination control technique for synchronizing a microgrid with the main electric power system during reconnection. A microgrid contains multiple distributed generators that must be controlled cooperatively to satisfy synchronization criteria. The proposed method uses a microgrid central controller that communicates with distributed generator controllers over a communication network. The controller calculates frequency, voltage, and phase offset commands to minimize differences between the microgrid and main grid. Simulation results using Matlab/Simulink show the technique can reliably synchronize the microgrid for network delays up to 200ms but not for delays of 500ms or greater.
Series of blackouts encountered in recent years in power system have been occurred because either of voltage or angle instability or both together was not detected within time and progressive voltage or angle instability further degraded the system condition, because of increase in loading. This paper presents the real-time assessment methodology of voltage stability using Phasor Measurement Unit (PMU) with observability of load buses only in power network. PMUs are placed at strategically obtained location such that minimum number of PMU’s can make all load buses observable. Data obtained by PMU’s are used for voltage stability assessment with the help of successive change in the angle of bus voltage with respect to incremental load, which is used as on-line voltage stability predictor (VSP). The real-time voltage phasors obtained by PMU’s are used as real time voltage stability indicator. The case study has been carried out on IEEE-14 bus system and IEEE-30 bus systems to demonstrate the results.
1) The document presents a study on implementing a fuzzy logic controller for an AC generator to improve transient stability.
2) A single machine infinite bus power system model is used to evaluate the fuzzy logic controller and compare it to a conventional power system stabilizer.
3) The fuzzy logic controller uses the speed deviation and acceleration of the generator rotor as inputs, and computes stabilizing signals based on these variables and fuzzy membership functions to dampen mechanical oscillations.
This document provides a review of low-voltage ride-through (LVRT) control methods for grid-connected photovoltaic systems. It discusses several control techniques that allow photovoltaic inverters to maintain grid functionality during faults. The techniques aim to regulate the unregulated output power of photovoltaic power stations during abnormal grid conditions. The review covers control approaches in different reference frames and for controlling positive, negative, and zero sequences. It identifies areas for further research and concludes with a brief summary and critique of existing LVRT control methods.
The document discusses various maximum power point tracking (MPPT) algorithms for wind energy systems. It describes three main MPPT control methods: tip speed ratio control, power signal feedback control, and hill-climb search control. For each control method, it provides the basic principles, block diagrams, and examples of implementations for different types of wind turbine generators including permanent magnet synchronous generators, squirrel cage induction generators, and doubly fed induction generators.
Comparison of Different Design Methods for Power System Stabilizer Design - A...ijsrd.com
In the past two decades, the utilization of supplementary excitation control signals for improving the dynamic stability of power systems has received much attention. In recent years, several approaches based on intelligent control and optimization techniques have been applied to PSS design problem. This paper introduces a review on the techniques applied on the conventional PSS design only. Power System Stabilizer (PSS) is the most cost effective approach of increase the system positive damping, improve the steady-state stability margin, and suppress the low-frequency oscillation of the power system. A PSS has to perform well under operating point variations. This paper introduces a review on the techniques applied on the conventional PSS design only. The techniques could be mainly classified into linear and nonlinear.
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.
Voltage profile Improvement Using Static Synchronous Compensator STATCOMINFOGAIN PUBLICATION
Static synchronous compensator (STATCOM) is a regulating device used in AC transmission systems as a source or a sink of reactive power. The most widely utilization of the STATCOM is in enhancing the voltage stability of the transmission line. A voltage regulator is a FACTs device used to adjust the voltage disturbance by injecting a controllable voltage into the system. This paper implement Nruro-Fuzzy controller to control the STATCOM to improve the voltage profile of the power network. The controller has been simulated for some kinds of disturbances and the results show improvements in voltage profile of the system. The performance of STATCOM with its controller was very close within 98% of the nominal value of the busbar voltage.
This document discusses several applications of fuzzy logic in electrical systems, including induction motor control, switched reluctance motor control, excitation control in automatic voltage regulators, and fuzzy logic control in an 18 bus power system. It focuses on using fuzzy logic for automatic voltage regulation, describing the typical components of a power system, challenges with conventional controllers, and presenting simulation results that demonstrate how a fuzzy logic controller can effectively regulate the voltage of a synchronous generator.
1. The document describes a hybrid wind and photovoltaic (PV) system connected to the main grid. It proposes using an adaptive neuro-fuzzy inference system (ANFIS) for maximum power point tracking (MPPT) of the PV array and a fuzzy logic controller for pitch angle control of the wind turbine.
2. Simulation results show that the ANFIS MPPT controller can track the maximum power point with less fluctuation and faster convergence compared to traditional MPPT methods. The fuzzy logic pitch controller provides faster response for high wind speeds, improving dynamic performance and preventing damage.
3. The complete hybrid system model is implemented in Matlab/Simulink, including the PV and wind systems, power converters
Differential game approach for the analysis of two area load frequency controlIRJET Journal
This document discusses using a differential game approach for analyzing load frequency control in a two-area power system. It first provides background on load frequency control and describes the traditional proportional-integral controller approach. It then introduces differential games as a tool for solving multi-objective optimization problems and proposes applying a cooperative differential game to control the two areas. The game would derive non-cooperative and cooperative equilibrium solutions to maintain system frequency stability and balance generation and loads across areas. Simulation results would compare this approach to traditional control methods.
Energy Balance in AC Is-landed Micro-grid by Frequency Bus Signaling MethodIRJET Journal
This document discusses energy balance techniques for microgrids with distributed energy resources like renewable energy sources and energy storage systems. It proposes a frequency bus signaling method to achieve energy balance without centralized communication. The method uses local frequency measurements to signal distributed power sources about how much power to generate in order to maintain the state of charge of energy storage below its maximum limit. This autonomous control strategy relies only on local signals and measurements to coordinate power generation and balance supply and demand within the microgrid.
The aim of this paper is to prove that fuzzy logic algorithm is a suitable control technique for fast processes such as electrical machines. This theory has been experimented on different kinds of electrical machines such as stepping motors, dc motors and induction machines (with 6 phases) and the experimental results show that the proposed fuzzy logic algorithm is the most suitable control technique for electrical machines since this algorithm is not time consuming and it is also robust between plant parameters variations.
This document proposes techniques to enhance the dynamic responses of a microgrid system containing photovoltaic and wind power generation. For photovoltaic power, an artificial neural network trained with genetic algorithm is used for maximum power point tracking, which can track the peak power point with high accuracy. For high wind speeds, a fuzzy logic controller is used for pitch angle control of the turbine, providing faster responses to reduce power fluctuations. The models are developed in Matlab/Simulink to analyze the microgrid system performance under different operating conditions.
The power supplied by photovoltaic DC–DC converter is affected by two factors, sun irradiance and temperature. Therefore, to improve the performance of the PV system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique present some difficulties in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). In this paper, we present a comparative study of the PV standalone system which is controlled by three techniques. The first one is conventional based on the observation and perturbation technique, the other are intelligent based on fuzzy logic according Mamdani and Takagi-Sugeno models. The investigations show that the fuzzy logic controllers provide the best results and Takagi-Sugeno model presents the lower overshoot value.
An IntelligentMPPT Method For PV Systems Operating Under Real Environmental C...theijes
The sun irradiance (G) and temperature (T) are the two main factors that affect the output power gained from the photovoltaic (PV) DC–DC converter. Therefore, to enhance the performance of the overall system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique, experience difficulty in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). The selection of the membership functions (MFs) and the fuzzy sets (FSs) numbers are crucial in the performance of the FLC based MPPT. Accordingly, this work presents numerous adaptive neuro-fuzzy systems to automatically adjustthe fuzzy logic controller membership functions as an alternative to the trial and error approach, which waste time and effort in MPPT design. For this purpose an adaptive neuro-fuzzy system is developed in MATLAB/Simulink to determine suitable MFs and the FSs for the fuzzy logic controller. The effects of different types of MFs and the FSs are deeply investigated using real data collected from the rooftop PV system. The investigations show that the fuzzy logic controller with a triangular membership function and seven fuzzy setsprovides the best results
Average dynamical frequency behaviour for multi-area islanded micro-grid netw...TELKOMNIKA JOURNAL
A micro-grid is a part of power system which able to operates in grid or islanding mode. The most important variable that able to give us information about the stability in islanded micro-grid network is the frequency dynamical responses. The frequency analysis for multi-area micro-grid network model may involve a complicated of mathematical equations. This makes the researcher intending to omit several unnecessary parameters in order to simplify the equations. The purpose of this paper is to show an approach to derive the mathematical equations to represent the average behavior of frequency dynamical responses for two different micro-grid areas. Both of networks are assumed to have non-identical distributed generator behavior with different parameters. The prime mover and speed governor systems are augmented with the general swing equation. The tie line model and the information of rotor angle was considered. Then, in the last section, the comparison between this technique with the conventional approach using centre of inertia (COI) technique was defined.
Improved dynamic performances of multi area reheat thermal agc power systems ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A review on SVC control for power system stability with and without auxiliary...journalBEEI
Since the beginning of the last century, power system stability has been recognized as a vital problem in securing system operation. Power system instability has caused many major blackouts. This paper reviewed the previous technical works consisting of various methods of optimization in controlling power system stability. The techniques presented were compared to optimize the control variables for optimization of power system stability. Power system stability enhancement has been investigated widely in literature using different ways. This paper is focusing on SVC performance for enhancing power system stability either through SVC controlled itself or SVC controlled externally by other controllers. Static VAR compensators (SVCs) are used primarily in power system for voltage control as either an end in itself or a means of achieving other objectives, such as system stabilization.The analysis on performance of the previous work such as advantages and findings of a robust method approach in each technique was included in this paper.
Coordinated Control of Interconnected Microgrid and Energy Storage System IJECEIAES
Several microgrids can be interconnected together to enhance the grid reliability and reduce the cost of supplying power to an island area where conventional power grid cannot be connected. Source and load demand do not properly balance always. Besides that, sometimes power and frequency fluctuation has occurred in MG at island mode. Need to design a special control for maintaining the state of charge (SoC) of energy storage system. This paper proposes a new power supply system for an island area that interconnects two microgrids with a single energy storage system (ESS). An algorithm has been proposed that control the microgrids energy storage system for spinning reserve and load power/frequency regulation purpose. The minimum loading constraints of diesel engine generator (DEG) is considered and the SOC of the ESS is properly maintained.
This present paper includes the study Load Frequency Control (LFC) of power systems with several nonlinearities
like Generation Rate Constraint(GRC) and Boiler Dynamics (BD) including Superconducting
Magnetic Energy Storage (SMES) units using Type-2 Fuzzy System (T2FS) controllers . Here, Load
frequency control problem is dealt with a three – area interconnected system of Thermal-Thermal-Hydal
power system by observing the effects and variations of dynamic responses employing conventional
controller, Type-1 fuzzy controller and T2FS controller considering incremental increase of step
pertubations by 10% in the load. The salient advantage of this controller is its high insensitivity to large
load changes and plant parameter variations even in the presence of non-linearities. As the non-linearities
were considered in the system, the conventional and classical Fuzzy controllers does not provide adequate
control performance with the consideration of above nonlinearities. To overcome this drawback T2FS
Controller has been employed in the system. Therefore, the efficacy of the proposed T2FS controller is
found to be better than that of conventional controller and Type-1 Fuzzy controller in cosidreration with
overshoot, settling time and robustness.
Coordination of Adaptive Neuro Fuzzy Inference System (ANFIS) and Type-2 Fuzz...IJECEIAES
This document summarizes a research paper that coordinates an adaptive neuro fuzzy inference system (ANFIS) and type-2 fuzzy logic system (T2FLS) power system stabilizer (PSS) to improve the stability of a large-scale power system. The ANFIS parameters are obtained through an offline training process, while the T2FLS parameters are determined based on knowledge gained from the ANFIS parameters. Simulation results show that the T2FLS-PSS is able to maintain stability better than conventional/ANFIS-PSS by decreasing peak overshoot and settling time for rotor speed and angle during local and inter-area oscillations. The T2FLS-PSS also performs better than other PSS types
This document summarizes a research paper that proposes a network-based coordination control technique for synchronizing a microgrid with the main electric power system during reconnection. A microgrid contains multiple distributed generators that must be controlled cooperatively to satisfy synchronization criteria. The proposed method uses a microgrid central controller that communicates with distributed generator controllers over a communication network. The controller calculates frequency, voltage, and phase offset commands to minimize differences between the microgrid and main grid. Simulation results using Matlab/Simulink show the technique can reliably synchronize the microgrid for network delays up to 200ms but not for delays of 500ms or greater.
Series of blackouts encountered in recent years in power system have been occurred because either of voltage or angle instability or both together was not detected within time and progressive voltage or angle instability further degraded the system condition, because of increase in loading. This paper presents the real-time assessment methodology of voltage stability using Phasor Measurement Unit (PMU) with observability of load buses only in power network. PMUs are placed at strategically obtained location such that minimum number of PMU’s can make all load buses observable. Data obtained by PMU’s are used for voltage stability assessment with the help of successive change in the angle of bus voltage with respect to incremental load, which is used as on-line voltage stability predictor (VSP). The real-time voltage phasors obtained by PMU’s are used as real time voltage stability indicator. The case study has been carried out on IEEE-14 bus system and IEEE-30 bus systems to demonstrate the results.
1) The document presents a study on implementing a fuzzy logic controller for an AC generator to improve transient stability.
2) A single machine infinite bus power system model is used to evaluate the fuzzy logic controller and compare it to a conventional power system stabilizer.
3) The fuzzy logic controller uses the speed deviation and acceleration of the generator rotor as inputs, and computes stabilizing signals based on these variables and fuzzy membership functions to dampen mechanical oscillations.
This document provides a review of low-voltage ride-through (LVRT) control methods for grid-connected photovoltaic systems. It discusses several control techniques that allow photovoltaic inverters to maintain grid functionality during faults. The techniques aim to regulate the unregulated output power of photovoltaic power stations during abnormal grid conditions. The review covers control approaches in different reference frames and for controlling positive, negative, and zero sequences. It identifies areas for further research and concludes with a brief summary and critique of existing LVRT control methods.
The document discusses various maximum power point tracking (MPPT) algorithms for wind energy systems. It describes three main MPPT control methods: tip speed ratio control, power signal feedback control, and hill-climb search control. For each control method, it provides the basic principles, block diagrams, and examples of implementations for different types of wind turbine generators including permanent magnet synchronous generators, squirrel cage induction generators, and doubly fed induction generators.
Comparison of Different Design Methods for Power System Stabilizer Design - A...ijsrd.com
In the past two decades, the utilization of supplementary excitation control signals for improving the dynamic stability of power systems has received much attention. In recent years, several approaches based on intelligent control and optimization techniques have been applied to PSS design problem. This paper introduces a review on the techniques applied on the conventional PSS design only. Power System Stabilizer (PSS) is the most cost effective approach of increase the system positive damping, improve the steady-state stability margin, and suppress the low-frequency oscillation of the power system. A PSS has to perform well under operating point variations. This paper introduces a review on the techniques applied on the conventional PSS design only. The techniques could be mainly classified into linear and nonlinear.
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.
Voltage profile Improvement Using Static Synchronous Compensator STATCOMINFOGAIN PUBLICATION
Static synchronous compensator (STATCOM) is a regulating device used in AC transmission systems as a source or a sink of reactive power. The most widely utilization of the STATCOM is in enhancing the voltage stability of the transmission line. A voltage regulator is a FACTs device used to adjust the voltage disturbance by injecting a controllable voltage into the system. This paper implement Nruro-Fuzzy controller to control the STATCOM to improve the voltage profile of the power network. The controller has been simulated for some kinds of disturbances and the results show improvements in voltage profile of the system. The performance of STATCOM with its controller was very close within 98% of the nominal value of the busbar voltage.
ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STABILIZER ON A SINGLE MACHINE ...EEIJ journal
In this paper the use of artificial neural network in power system stability is studied. A predictive
controller based on two neural networks is designed and tested on a single machine infinite bus system
which is used to replace conventional power system stabilizers. They have been used for decades in
power system to dampen small amplitude low frequency oscillation in power systems. The increases in
size and complexity of power systems have cast a shadow on efficiency of conventional method. New
control strategies have been proposed in many researches. Artificial Neural Networks have been studied
in many publications but lack of assurance of their functionality has hindered the practical usage of them
in utilities. The proposed control structure is modelled using a novel data exchange established between
MATLAB and DIgSILENT power factory. The result of simulation proves the efficiency of the proposed
structure.
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.
PLACEMENT OF POWER SYSTEM STABILIZER (PSS) IN NIGERIA 28-BUS POWER NETWORK FO...IRJET Journal
This document discusses the placement of power system stabilizers (PSS) in Nigeria's 28-bus power network to improve voltage and dynamic stability. It first provides an overview of issues like voltage problems, steady-state instability, and transient stability challenges in Nigeria's existing 10-generator 330kV power system without PSS. It then describes the mathematical modeling of PSS and its design to dampen generator rotor oscillations. Finally, it implements PSS in two test cases - a 3-bus and the 28-bus Nigeria network using MATLAB/SIMULINK and evaluates the improvement in maximum loading points and stability with PSS.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
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
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.
IRJET- Comparative Study on Angular Position and Angular Speed in 36 Rules of...IRJET Journal
This document discusses the use of fuzzy logic controllers for power system stabilizers. It begins with an introduction to power system stability and the need for stabilizers to damp oscillations. A single machine infinite bus model is described and a conventional lead-lag power system stabilizer model is presented. Next, the design of a fuzzy logic based power system stabilizer is outlined, including input/output variables, rules, and membership functions. A 36 rule fuzzy logic controller is presented. Simulation results in Simulink are shown comparing performances for positive and negative K5 values. Finally, the fuzzy logic stabilizer is compared to other techniques like ANN, GA and PSO, showing it reduces oscillations faster. The conclusion is that the fuzzy logic approach
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
Power System MIMO Identification for Coordinated Design of PSS and TCSC Contr...Reza Pourramezan
Authors: Reza Pourramezan, Sadegh Vaez-Zadeh, and Hamid Reza Nourzadeh
Published in 2007 IEEE Power Engineering Society General Meeting (PES)
DOI: 10.1109/PES.2007.385692
Design of power system stabilizer for damping power system oscillationsIOSRJEEE
The problem of the poorly damped low-frequency (electro-mechanical) oscillations of power systems has been a matter of concern to power engineers for a long time, because they limit power transfers in transmission lines and induce stress in the mechanical shaft of machines. Due to small disturbances, power systems experience these poorly damped low-frequency oscillations. The dynamic stability of power systems are also affected by these low frequency oscillations. With proper design of Power System Stabilizer (PSS), these oscillations can be well damped and hence the system stability is enhanced. The basic functions of the PSS is to add a stabilizing signal that compensates the oscillations of the voltage error of the excitation system during the dynamic/transient state, and to provide a damping component when it’s on phase with rotor speed deviation of machine. Studies have shown that PSS are designed to provide additional damping torque, for different operation point normal load, heavy load and leading to improve power system dynamic stability.
Model based PI power system stabilizer design for damping low frequency oscil...ISA Interchange
This paper explores a two-level control strategy by blending a local controller with a centralized controller for the low frequency oscillations in a power system. The proposed control scheme provides stabilization of local modes using a local controller and minimizes the effect of inter-connection of sub-systems performance through a centralized control. For designing the local controllers in the form of proportional-integral power system stabilizer (PI-PSS), a simple and straight forward frequency domain direct synthesis method is considered that works on use of a suitable reference model which is based on the desired requirements. Several examples both on one machine infinite bus and multi-machine systems taken from the literature are illustrated to show the efficacy of the proposed PI-PSS. The effective damping of the systems is found to be increased remarkably which is reflected in the time-responses; even unstable operation has been stabilized with improved damping after applying the proposed controller. The proposed controllers give remarkable improvement in damping the oscillations in all the illustrations considered here and as for example, the value of damping factor has been increased from 0.0217 to 0.666 in Example 1. The simulation results obtained by the proposed control strategy are favorably compared with some controllers prevalent in the literature.
A robust dlqg controller for damping of sub synchronous oscillations in a ser...eSAT Journals
Abstract This paper investigates the use of Discrete Linear Quadratic Gaussian (DLQG) Compensator to damp sub synchronous oscillations in a Thyrisor Controlled Series Capacitor (TCSC) compensated power system. The study is conducted on IEEE First Benchmark Model (FBM) in which, TCSC is modelled as a discrete linear time-invariant modular unit in the synchronously rotating DQ reference frame. This modular TCSC is then integrated with the Linear Time Invariant (LTI) model of the rest of the system. The design of DLQG includes the design of a Kalman filter for full state estimation and a full state feedback for control. Since the order of the controller is as large as the order of the system considered here(27 states), the practical implementation of the controller is difficult. Hence by using Hankels norm approximation technique, the order of the controller is reduced from 27 to 15 without losing the significant system dynamics. The eigen analysis of the system shows that the use of DLQG can damp torsional oscillations as well as the swing mode oscillations simultaneously, which is practically difficult for a conventional sub-synchronous damping controller. The performance of the system with DLQG is appreciable for all operating conditions and it shows the robustness of the controller. Index Terms: Sub-Synchronous Resonance (SSR), Torsional Oscillations, Thyristor Controlled Series Capacitor (TCSC), Discrete Linear Quadratic Gaussian(DLQG)Compensator, Model Order Reduction (MOR).
A robust dlqg controller for damping of sub synchronous oscillations in a se...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A hybrid bacterial foraging and modified particle swarm optimization for mode...IJECEIAES
This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
Performance assessment of an optimization strategy proposed for power systemsTELKOMNIKA JOURNAL
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
Energy Management System in Microgrid with ANFIS Control Scheme using Heurist...IRJET Journal
1. Premise: The document discusses an energy management system for a microgrid that uses an adaptive neuro-fuzzy inference system (ANFIS) control scheme.
2. Consequence: It is proposed that ANFIS control can efficiently manage power balancing in a microgrid system with renewable energy sources and an energy storage system. The ANFIS control scheme is simulated in MATLAB/Simulink to validate its effectiveness in mitigating power fluctuations during varying loads.
3. Resolution: The results of the simulation demonstrate that the proposed ANFIS control scheme can function as both an energy management system and battery management system for a microgrid system with renewable energy sources.
Similar to Neural Network-Based Stabilizer for the Improvement of Power System Dynamic Performance (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
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
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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neural network synthesized to give in its output layer the optimal controller parameters adaptive
to system operating condition and configuration. Although, the control coordination scheme has
successfully been applied in enhancing and maintaining stability of a small power system
(system that containing four synchronous machines), its performance, however, on larger
interconnected multi-machine power systems is still a problem and consequently needs to be
addressed and investigated.
Therefore, against the above background, the objective of the present paper is to apply
and extend the adaptive control coordination scheme proposed in [15] to larger interconnected
multi-machine electric power systems. The present paper is still taking advantage of the
important features of the scheme in [15] as follows:
1. Representation of power system configuration by reduced nodal impedance matrix.
In this way, any power system configuration can be represented directly and systematically. The
matrix is formed only for power network nodes that have direct connections to synchronous
generators. The reduced nodal impedance matrix can be derived very efficiently from the power
system nodal admittance matrix and sparse matrix operations. Elements of this matrix are
inputs to the neural network,
2. Representation of power system loading by active- and reactive- power of the
synchronous generators. These values (available from measurements) are also inputs to the
neural network, and (iii) the optimal controller parameters adaptive to system operating
condition and configuration are outputs of the neural network.
However, in order to successfully apply the control coordination scheme in larger
interconnected multi-machine power system environment, the present paper utilizes the
following solutions and modifications to the scheme in [15]:
1. Reducing the neural network inputs number by discounting the real parts of the
reduced nodal impedance matrix, given that the parameters of the transmission circuits are
dominated by reactances.
2. Reducing the neural network outputs number by adapting only the controller gains to
the prevailing system condition, and keep the controller time constants at the constant values.
This modification is possible because the controller gains are more sensitive to system changes
than the time constants [15]. By applying the above modifications, the size of the modified
neural network and its training can be greatly simplified and kept to be minimal.
The application of the above two modifications are the main contributions of the present
paper. AIso, in the present paper, the modified neural network scheme is trained and tested with
a wide range of credible power system operating conditions and configurations. For all of the
tests considered, the controller parameters obtained from the trained neural network are verified
by both eigenvalue calculations and time-domain simulations, which confirm that good
dampings of the rotor modes are achieved, and the decrease in system dynamic performances
arising from the use of fixed-parameter (non-adaptive) controllers will be removed
2. Research Method
2.1. Power System Stabilizer (PSS)
Figure 1 shows the general structure of a PSS [16] which is adopted in this paper. The
structure consists of a gain block, a washout, lead-lag blocks and a limiter. A washout term/filter
(i.e. with a time derivative operator) in the PSS structure is needed to guarantee that the PSS
responds only to disturbances, and does not respond to any steady-state condition, when speed
or power is input.
In the present work, the machine rotor speed is used for the PSS input. The PSS output
is added to the exciter voltage error signal of the synchronous machine and served as a
supplementary signal to add to the system damping.
Figure 1. PSS control block diagram
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2.2. Development of Neural Network-Based Stabilizer
2.2.1. General Concept of Neural Network
The relationship among the optimal controller (PSSs) parameters and power system
operating condition including system configuration is, in general, a nonlinear one. The present
paper draws on the key property of the multilayer feedforward neural network, which is that of
nonlinear multi-variable function representation [17]. The neural network is used for the mapping
between the power system configurations and/or operating conditions and optimal controller
parameters.
Figure 2 is shown the general structure of the multilayer feedforward neural network
which is adopted to represent the nonlinear relationship between the optimal controller
parameters and power system operating condition together with configuration. There are two
separate sets of nodes in the inputs layer in Figure 2. The first set has n nodes the inputs to
which are obtained from the real and imaginary parts of the reduced nodal impedance matrix.
These inputs represent power system configuration. If there are Ng generator nodes, the
number of input nodes in the first set is Ng2
+Ng, when the symmetry in the nodal impedance
matrix is exploited. The second set of inputs comprises active- and reactive-power of each and
every generator. There will be 2Ng input nodes in the second set. These inputs in the second
set represent power system operating condition. Therefore, the total number of inputs is
Ng2
+3Ng. If the real parts of the reduced nodal impedance matrix are discounted, then the total
number of inputs will be 0.5Ng2
+2.5Ng.
Figure 2. Structure of the neural network
The nodes in the output layer of the neural network structure in Figure 2 give the
optimal values of the parameters of PSSs. The structure in Figure 2 assumes that there are M
controller parameters to be tuned online. On this basis and with the controller in Figure 1, the
number of output parameters from the neural network in Figure 2 is 6Nc, where Nc is the
number of PSSs. If only the PSS gains are to be tuned online, than the number of output
parameters will be Nc.
The number of hidden layers, the number of nodes in each hidden layer and the
weighting coefficients of the connections between nodes in the structure of Figure 2 are to be
determined by neural network training, and verified by testing which will be discussed in
Sections 2.2.3 and 2.2.4.
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2.2.2. Overall Structure
In Figure 3 is shown the overall structure of which the neural adaptive controller
described in Section 2.2.1 is a part. For online tuning of the parameters of PSSs, the inputs
required are as follows:
1. Circuit-breaker and isolator status data
2. Power network branch parameters
3. Generator active- and reactive-power
The response of the trained neural network gives the optimal parameters for the PSSs.
The feedback inputs to these controllers are generator speeds, as in the case of fixed-
parameter controllers.
Figure 3. Neural network-based stabilizer
2.2.3. Training Procedure for Neural Network-Based Stabilizer
The training set is generated using the optimisation-based control coordination method
in [4]. A brief description of the method is given in the following.
The method is based on a constrained optimisation in which the objective function
formed from the real part of eigenvalues of selected modes is minimised. The method does not
require any special eigenvalue/eigenvector calculation software. Eigenvalue-eigenvector
equations are represented in terms of equality constraints in the optimisation. Based on the
linear independence of eigenvectors, additional equality constraints are derived and included in
the optimisation to guarantee distinct modes at the convergence. Inequality constraints related
to minimum damping ratios required and controller parameter limits are represented in the
control coordination.
For a given power system, a wide range of credible operating conditions and
configurations which include those arise from contingencies is considered in the training data
generation. For the ith
training case, the pair of specified input and output vectors is ii t,p .
Based on the structure in Figure 2, the input vector pi is:
NiT
.....,2,1,; mi2i1ii p,.....,p,pp (1)
In which N is the total number of training cases.
The target output vector ti for the ith
training case is the optimal controller parameters
vector for the power system with the operating condition and configuration specified by the input
vector pi.
The requirement in the training is to minimise the difference between the target output
vector ti and response of the neural network in Figure 2. For N training cases, it is proposed to
minimise the following mean square error (MSE):
N
1i
T
N
1
F )a(t)a(t(x) iiii (2)
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In (2), ai is the neural network response which has the following form, based on the
structure in Figure 2:
NiM21
T
.....,2,1,; iiii a,.....,a,aa (3)
Vector x in (2) is the vector of weighting coefficients of the connections in the neural
network to be identified. Minimising the error function F(x) with respect to x gives the weighting
coefficient vector. In the present work, the Levenberg-Marquardt algorithm which is a second-
order method with a powerful convergence property is adopted for minimising F(x) in (2). One of
the criteria for the convergence in training is that the error function F(x) has to be less than a
specified tolerance.
In addition to the training performance expressed in terms of error function F(x), the
controller parameters obtained from the trained neural network are also used for calculating the
damping ratios of the rotor modes, which are then compared with the optimal damping ratios
obtained at the stage of training data generation. The convergence in training is confirmed when
both the error function F(x) and the damping ratio comparison satisfy the specified tolerances.
2.2.4. Neural Network Testing and Sizing
In addition to forming the training data set, a separate testing data set is also required.
The procedure for testing data generation is similar to that of training where the optimization-
based control coordination method in [4] is used.
The trained neural network in Section 2.2.3 is then tested with the testing data set. The
interaction among the training, testing and sizing the neural network is explained in the flowchart
of Figure 4.
Figure 4 Flowchart for training, testing and sizing of the neural network
3. Results and Analysis
3.1. Power System Structure
The system in the study is based on the 10-machine 40-node power system of Figure 5
[18]. Initial investigation, where the damping controllers (PSSs) are not included in the system,
has been carried out to determine the system oscillation damping without the controllers. Modal
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analysis used in the investigation shows that there are nine electromechanical modes of
oscillations.
The investigation also confirms that the system has poor damping. This low system
damping indicates poor system dynamic performance and stability. Stabilization measure is,
then, required for improving the damping of the oscillations. Therefore, in order to improve the
stability, it is proposed to install PSSs in the system (each generator is equipped with PSS).
3.2. Design of the Neural Network-Based Stabilizer
3.2.1. Neural Network Training and Test Data
The key requirement is to design a neural controller that has the capability of
generalizing with high accuracy from the training cases. This requirement is achieved through
the neural network training, testing and sizing referred to in Sections 2.2.3 and 2.2.4 based on
the selection of the training and testing data sets. The neural network training set should be
representative of the cases described by credible system contingencies and changes in system
operating conditions.
The possible contingencies of the system in Figure 5 for line outages and load
variations are shown in Table 1 and 2 respectively. The input and output pairs for neural
network training and testing cases are generated from the combinations of these contingencies
and operating conditions.
For the system in Figure 5, the number of neural network inputs, as determined on the
basis of Section 2.2.1, is 75. As mentioned also in Section 2.2.1, in order to simplify the neural
network training and testing, only the controller gains are to be tuned online. Therefore, 10
linear neurons are needed in the output layer (1 for each PSS controller).
Figure 5. 10-machine 40-node power system
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Table 1. Line Outages Cases
No. Line No. Line No. Line
1.1 L1 1.13 L13 1.24 L24
1.2 L2 1.14 L14 1.25 L25
1.3 L3 1.15 L15 1.26 L26
1.4 L4 1.16 L16 1.27 L27
1.5 L5 1.17 L17 1.28 L28
1.6 L6 1.18 L18 1.29 L29
1.7 L7 1.19 L19 1.30 L30
1.8 L8 1.20 L20 1.31 L31
1.9 L9 1.21 L21 1.32 L32
1.10 L10 1.22 L22 1.33 L33
1.11 L11 1.23 L23 1.34 L34
1.12 L12
Table 2. Variations of Load
No. Load Demand at All Nodes No. Load Demand at All Nodes
2.1 50% of Base Load 2.12 105% of Base Load
2.2 55% of Base Load 2.13 110% of Base Load
2.3 60% of Base Load 2.14 115% of Base Load
2.4 65% of Base Load 2.15 120% of Base Load
2.5 70% of Base Load 2.16 125% of Base Load
2.6 75% of Base Load 2.17 130% of Base Load
2.7 80% of Base Load 2.18 135% of Base Load
2.8 85% of Base Load 2.19 140% of Base Load
2.9 90% of Base Load 2.20 145% of Base Load
2.10 95% of Base Load 2.21 150% of Base Load
2.11 100% of Base Load
The load demands in the system are varied in the representative range between
minimum and maximum values. It has been taken that the load demands at all nodes follow
similar patterns. However, any different patterns of load demand variations, for example, in
areas with different time zones, when they arise, can be included in the data set without
difficulty.
For each contingency, the reduced nodal impedance matrix for the generator nodes
1-10 (including the slack bus) is formed. The power generations including those at the slack bus
and the reactive-power which are obtained from load-flow studies and the elements of the
reduced nodal impedance matrix are used as the neural network input data. The optimal
controller parameters are also determined for each case using the method described in [4].
These optimal controller parameter values are used as the specified network output data.
In applying the optimal control coordination [4] for training and test data generation, the
sum of the squares of the real parts of all of the eigenvalues of the electromechanical modes is
maximised, with the constraints that the minimum damping ratio of the electromechanical
modes is 0.1. The minimum value of 0.1 is chosen because, as mentioned also in [19, 20], the
damping ratio lower than 0.1 is considered unacceptable.
The cases generated from Table 1 and 2 are sub-divided into the training set and test
set. For the training set, line outage cases 1.1 – 1.3, 1.5 – 1.16, 1.18 – 1.30, and 1.32 – 1.34
together with load demand variations in cases 2.2 – 2.10, and 2.12 – 2.20 are selected. The
remaining cases of line outages and load demand variations in Tables 2 and 3 are used for the
test set
3.2.2. Training, Testing and Sizing the Neural Network
In the present work, the neural network is initially assumed to have one hidden layer
and the number of hidden nodes is taken to be 5. The size of the neural network is then
adjusted according to the procedure described in Section 2.2.4.
The performance goals specified in terms of error function F(x) of 0.004 (for training)
and 0.006 (for testing) are used. The maximum differences between the optimal damping ratio
and the damping ratio calculated using neural network outputs of 0.03 (for training) and 0.05 (for
testing) are also used as the performance goals. Maximum number of epoch of 100 is specified
for the network training. Several network sizes (i.e. number of hidden neurons) are investigated
to achieve the performance goals. Based on the investigation, it is found that the network with
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10 hidden neurons in one hidden layer satisfies the convergence criteria. On this basis, the
trained and tested neural network is used in the application mode, and its dynamic performance
is evaluated by simulation in the following sections
3.3. Dynamic Performance of the System in the Study
Table 3 shows the comparison of modal response characteristics (electromechanical
mode eigenvalues, frequencies and damping ratios) between non-adaptive (fixed-parameter)
and adaptive (neural network-based) controllers of the system in Figure 5 for a range of
contingencies and operating conditions. For non-adaptive controller, the controller parameters
derived from the base case design are used for all of the contingency cases and load changes.
It is to be noted that, as the system in Figure 5 has 9 electromechanical modes, only three
modes with the lowest damping ratios are taken and shown for the comparison.
The base case (referred to as case 1 in Table 3) is that with the full system (no line
outages) in Figure 5, and load demands at all nodes are at their base load values. The
comparison in Table 3 for case 1 confirms that the damping ratios for the electromechanical
modes achieved by the neural adaptive controller are closely similar to those obtained from the
fixed-parameter controllers (i.e. non-adaptive) designed with the system configuration and
operating condition specified in the base case.
Table 3. Dynamic Performances of Controllers
No Case
Non-Adaptive Controller [4] Adaptive Controller
Eigenvalues
(pu)
Freq.
(Hz)
Damp.
Ratio
Eigenvalues
(pu)
Freq.
(Hz)
Damp.
Ratio
1
Base Case
-1.2390 ± j11.1349
-0.9672 ± j9.0460
0.8482 ± j8.3001
1.77
1.44
1.32
0.11
0.11
0.10
-1.2401 ± j11.1477
-0.9831 ± j9.0753
-0.8687 ± j8.3148
1.17
1.44
1.32
0.11
0.11
0.10
2
System Load:
150% of Base Load
-1.0776 ± j11.7520
-1.1584 ± j11.7529
-0.6209 ± j9.6381
1.87
1.77
1.53
0.09
0.10
0.06
-1.1923 ± j11.8441
-1.2093 ± j11.7463
-0.9862 ± j10.2434
1.89
1.87
1.63
0.10
0.10
0.10
3
System Load:
140% of Base Load
-1.1191 ± j11.6405
-1.2015 ± j11.6575
-0.7120 ± j9.4949
1.85
1.86
1.51
0.10
0.10
0.07
-1.1359 ± j11.5581
-1.2327 ± j11.4346
-1.0067 ± j10.0525
1.84
1.82
1.60
0.10
0.11
0.10
4
Line L4 Out
-1.2377 ± j11.1359
-0.9618 ± j9.0562
-0.5993 ± j8.2917
1.77
1.44
1.32
0.11
0.11
0.07
-1.3023 ± j11.1006
-1.1612 ± j10.8826
-0.8284 ± j8.4445
1.77
1.73
1.34
0.12
0.11
0.10
5 Line L34 Out
-1.2375 ± j11.1368
-0.9594 ± j9.0604
-0.7685 ± j8.3336
1.77
1.44
1.33
0.11
0.11
0.09
-1.2219 ± j11.1043
-0.9916 ± j9.1219
-0.8462 ± j8.3497
1.77
1.45
1.33
0.11
0.11
0.10
In cases 2 and 3 of Table 3, the load demands at all nodes increase to 150% and 140%
of base load respectively while the system configuration remains as that of the base case. With
non-adaptive controllers, the damping ratios of the electromechanical modes decrease
noticeably in comparison with those in the base case (there are modes with unacceptable
damping ratios or lower than 0.1). However, with the neural adaptive controller, the damping
ratios are maintained at the levels similar to those of the base case.
Further comparison in cases 4 and 5 of Table 3 focuses on contingencies where one
transmission circuit is disconnected. The load demands are those in the base case. In case 4
where there is an outage of transmission line L4 in Figure 5, there is a substantial reduction in
the mode damping in comparison with the base case. The damping ratio of this mode is
reduced to 0.07, compared to 0.10 in the base case. With the adaptive controller, the damping
ratios of all of the electromechanical modes are almost not affected by the outage, in
comparison with those in the base case, as indicated in Table 3.
The outage of transmission line L34 in case 5 of Table 3 affects the damping of the
electromechanical modes marginally when the non-adaptive controllers are used. The damping
ratio of 0.10 in the base case is now reduced to 0.09 in the outage case 5. The robustness of
the adaptive controller in this outage case is confirmed by the results of Table 3. The controller
parameters determined by the trained neural network are able to adapt to the new system
configuration for maintaining the modal damping ratios at the levels similar to those in the base
case.
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3.4. Time-Domain Simulations
In order to further validate the performance of the neural network-based controller, time-
domain simulations are carried out for the selected contingency cases (i.e. 50% load increase
and line L4 outage). The time-step length of 50 ms is adopted for the simulations. The
descriptions of the contingency cases and the disturbances used to initiate the transients for
each case are given in Table 4.
In Figure 6 is shown the system transients following the disturbance for the first
contingency case (i.e. case A). It is to be noted that for this contingency, the damping ratio of
the weakest mode is 0.06 when the non-adaptive controller is used (see Table 3). As the
participations of generators G1 and G3 to this mode are more dominant, then the relative speed
transients between generators G1 and G3 are used in forming the responses in Figure 6. From
the responses, it can be seen that, with non-adaptive controller, the system oscillation is poorly
damped and takes a considerable time to reach a stable condition. With the neural network-
based (adaptive) controller, the system reaches steady-state condition in 5 – 6 s subsequent to
the disturbance for the contingency case considered (see Figure 6). Further comparison in
terms of the transients in the torque angle of generator G1 relative to that of generator G3 are
given in Figure 7. The comparison confirms the improvement in electromechanical oscillation
damping when the neural network-based controller is used.
In Figure 8 and 9 are shown the relative speed transient and torque angle transient for
contingency case B in Table 4 respectively. For this contingency case, results similar to
contingency case A are obtained. These results also confirm the improvement in
electromechanical oscillation damping when the neural network-based controller is used.
Table 4. Descriptions of Contingency Cases and Disturbances
Case Contingency Description Disturbance Description
A 50% sudden load increase at
all nodes.
Three-phase fault near node N33 on line L10. The
fault is initiated at time t = 0.10 s, and the fault
clearing time is 0.05 s.
B Line L4 has to be
disconnected to clear the
fault.
Three-phase fault near node N33 on line L10. The
fault is initiated at time t = 0.10 s, and the fault
clearing time is 0.05 s.
Figure 6. Relative speed (G1-G3) transients
for contingency case A
Figure 7. Relative torque angle (G1-G3)
transients for contingency case A
10. TELKOMNIKA ISSN: 1693-6930
Neural Network-Based Stabilizer for the Improvement of Power System... (Rudy Gianto)
993
Figure 8. Relative speed (G3-G10) transients
for contingency case B
Figure 9. Relative torque angle (G3-G10)
transients for contingency case B
4. Conclusion
An adaptive control algorithm and procedure have been derived and developed for
online tuning of the PSSs. The procedure is based on the use of a neural network which adjusts
the parameters of the controllers to achieve system stability and maintain optimal dampings as
the system operating condition and/or configuration changes.
The neural network-based controller trained for a representative power system has
been comprehensively tested to verify its dynamic performance. Both eigenvalue calculations
and time-domain simulations are applied in the testing and verification. Many comparative
studies have been carried out to quantify the improved performance of the adaptive controller in
comparison with that achieved with fixed-parameter controllers. The results confirm that, by
using the neural network-based controller, the decrease in system dampings and dynamic
performances arising from the use of fixed-parameter controllers will be removed.
Acknowledgements
The authors would like to express special appreciation to the Ministry of Research,
Technology and Higher Education (KEMENRISTEK DIKTI INDONESIA) for funding the
research reported in this paper.
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