Network Based Power System Stabilizer Research Articles

Artificial Neural Network Based Power System Stabilizer on a Single Machine Infinite Bus System Modelled in Digsilent Powerfactory and Matlab

Artificial Neural Network Based Power System Stabilizer on a Single Machine Infinite Bus System Modelled in Digsilent Powerfactory and Matlab

Simulation of Artificial Neural Network Based Power System Stabilizer using Digsilent Power Factory and Matlab Interface

Simulation of Artificial Neural Network Based Power System Stabilizer using Digsilent Power Factory and Matlab Interface

Online Tuning of Power System Stabilizers using Fuzzy Logic Network with Fuzzy C-Means Clustering

Power system stabilizers (PSS) have been widely used to enhance damping due to the electromechanical low frequency oscillations occurrence in power systems. In this paper, a new method is used for the online tuning of parameters of conventional power system stabilizers (CPSS) using fuzzy logic. Fuzzy logic enables mathematical modeling and computation of some nonlinear parameters of the system, which are usually, derived empirically by utilization of expert knowledge rules. Various literatures has shown that fuzzy logic controller is one of the most useful methods for expert knowledge utilization. This type of controller is adaptive in nature and can be used successfully as a power system stabilizer. The design of fuzzy logic controllers is mainly based on fuzzy rules and input/output membership functions. Simple and efficient clustering algorithms allow data classification in distinct groups using distance and/or similarity functions. In the present paper, the optimum generation of fuzzy rules base using Fuzzy C-means (FCM) clustering technique is used. In fact, data are classified and the number of fuzzy rules which depends on convergence radius is determined. Finally, the performance of proposed FCM controller is compared with that of conventional controller. The active power, reactive power and bus voltages used as inputs to the fuzzy logic network based power system stabilizer and the parameters of the optimum stabilizer , i.e. gain factor as well as time constants of the lead/lag compensator, are the outputs of the proposed system. The design method has been successfully implemented on a single machine power system connected to an infinite bus over various operating conditions.

Online Tuning of Power System Stabilizers Using Fuzzy Logic Network with Fuzzy C-Means Clustering Prediction: A Case Study

Power system stabilizers (PSS) has been widely used to enhance damping due to the electromechanical low frequency oscillations occurrence in power systems. In this paper, a new method is used for the online tuning of parameters of conventional power system stabilizers (CPSS) using fuzzy logic. Fuzzy logic enables mathematical modeling and computation of some nonlinear parameters of the system, which are usually derived empirically by utilization of expert knowledge rules. Various literatures has shown that fuzzy logic controller is one of the most useful methods for expert knowledge utilization. This type of controller is adaptive in nature and can be used successfully as a power system stabilizer. The design of fuzzy logic controllers is mainly based on fuzzy rules and input/output membership functions. Simple and efficient clustering algorithms allow data classification in distinct groups using distance and/or similarity functions. In the present paper, the optimum generation of fuzzy rules base using Fuzzy C-means (FCM) clustering technique is used. In fact, data are classified and the number of fuzzy rules which depends on convergence radius is determined. Finally, the performance of proposed FCM controller is compared with that of conventional controller. The active power, reactive power and bus voltages used as inputs to the fuzzy logic network based power system stabilizer and the parameters of the optimum stabilizer , i.e. gain factor as well as time constants of the lead/lag compensator, are the outputs of the proposed system. The design method has been successfully implemented on a single machine power system connected to an infinite bus over various operating conditions.

Design of an adaptive neural network based power system stabilizer

Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this paper presents an indirect adaptive neural network based power system stabilizer (IDNC) design. The proposed IDNC consists of a neuro-controller, which is used to generate a supplementary control signal to the excitation system, and a neuro-identifier, which is used to model the dynamics of the power system and to adapt the neuro-controller parameters. The proposed method has the features of a simple structure, adaptivity and fast response. The proposed IDNC is evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate its effectiveness and robustness.

ANN Based Power System Stabilizers for Large Synchronous Generators

This paper presents an artificial neural network based power system stabilizer (ANNPSS) for excitation control for a large synchronous generator. The generator operates over a wide range of operating conditions and is subjected to different types of disturbances and emergency states. A 300 MW turbogenerator is used to generate appropriate training data for the ANN controller. Off-line simulations using a suitable conventional PSS to control the generator for different working conditions are used to generate training input-output pairs. A multi-layered back propagation (BP) ANN is utilised to design the ANN based controller where speed error deviation and the incremental change in machine terminal voltage are fed to the ANNPSS controller. The proposed ANNPSS and conventional PSS are compared and test results indicate that the ANN based controller is more adaptive and flexible than conventional stabilizers and show a good performance over a wide range of operating conditions and disturbances.

Closure to discussion of "Nneural network based power system stabilizer suitable for on-line training"a practical case study for egat system"

Closure to discussion of "Nneural network based power system stabilizer suitable for on-line training"a practical case study for egat system"

Discussion of "A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system" [and reply

S.N. Singh et al. comment on the paper by B. Changaroon et al. (see ibid., vol.15, no.1. p.103-9, 2000). They ask for the original authors' opinions on five points. The original authors reply to the comments.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS.

99/03189 A fuzzy basis function network based power system stabilizer for generator excitation control

To address the problem of fossil fuel usage and high greenhouse gas emissions at the Missouri University of Science and Technology campus, using of alternative fuels and renewable energy sources can lower energy consumption and greenhouse gas emissions. Biogas, produced by anaerobic digestion of wastewater, organic waste, agricultural waste, industrial waste, and animal by-products is a potential source of renewable energy. In this work, we have discussed the design of CHHP system for the campus using local resources. An energy flow and resource availability study is performed to identify the type and source of feedstock required to continuously run the fuel cell system at peak capacity. Following the resource assessment study, the team selects FuelCell Energy DFC1500TM unit as a MCFC. The CHHP system provides electricity to power the university campus, thermal energy for heating the anaerobic digester, and hydrogen for transportation, back-up power and other needs. In conclusion, the CHHP system will be able to reduce fossil fuel usage, and greenhouse gas emissions at the university campus.

A fuzzy basis function network based power system stabilizer for generator excitation control

A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper. The proposed FBFN-based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to retune the PSS parameters in real-time, based on machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious FBFN model. Time domain simulations of a synchronous machine equipped with the proposed stabilizer subject to major disturbances are investigated. The performance of the proposed FBFN PSS is compared with that of a conventional power system stabilizer (CPSS) to demonstrate the superiority of the proposed stabilizer. The effect of parameter changes on the proposed stabilizer performance is also examined. The results show the robustness of the proposed FBFN PSS and its capability to enhance system damping over a wide range of operating conditions.

Adaptive tuning of power system stabilizers using radial basis function networks

Abstract A novel approach for on-line adaptive tuning of power system stabilizer (PSS) parameters using radial basis function networks (RBFNs) is presented in this paper. The proposed RBFN is trained over a wide range of operating conditions and system parameter variations in order to re-tune PSS parameters on-line based on real-time measurements of machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious RBFN model. The simulation results of the proposed radial basis function network based power system stabilizer (RBFN PSS) are compared to those of conventional stabilizers in case of a single machine infinite bus (SMIB) system as well as a multimachine power system (MMPS). The effect of system parameter variations on the proposed stabilizer performance is also examined. The results show the robustness of the proposed RBFN PSS and its ability to enhance system damping over a wide range of operating conditions and system parameter variations. The major features of the proposed RBFN PSS are that it is of decentralized nature and does not require on-line model identification for tuning process. These features make the proposed RBFN PSS easy to tune and install.

97/02890 Speed deviation driven adaptive neural network based power system stabilizer

97/02890 Speed deviation driven adaptive neural network based power system stabilizer

Speed deviation driven adaptive neural network based power system stabilizer

The paper presents an online adaptive artificial neural network (ANN) based power system stabilizer (PSS). The proposed controller is first trained offline using a pole placement based state feedback gain technique at different operating points. The trained ANN parameters (weights and biases) are updated and tuned online using the speed deviation as the reinforcement signal. The proposed PSS is tested at different operating conditions and a variety of regulator gains. The digital results validate the effectiveness and reliability of the new PSS in terms of fast system response under different loading conditions compared with the conventional PI controller and the modern control theory approach of pole placement.

A neural network-based power system stabilizer using power flow characteristics

A neural network-based power system stabilizer (neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The use of power flow dynamics provides a PSS for a wide range of operation with reduced size neural networks. The neuro-PSS consists of two neural networks: neuro-identifier and neuro-controller. The low-frequency oscillation is modeled by the neuro-identifier using the power flow dynamics, then a generalized backpropagation-through-time (GBTT) algorithm is developed to train the neuro-controller. The simulation results show that the neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.