This paper presents the design of fuzzy logic power system stabilizers using genetic algorithms in multimachine power system In the proposed fuzzy expert system, generator speed deviation and acceleration are chosen as input signals to fuzzy logic power system stabilizer In this approach gains, centers of membership functions and the parameters of the fuzzy logic controllers have been tuned using genetic algorithm Incorporation of GA in the design of fuzzy logic power system stabilizer will add an intelligent dimension to the stabilizer and significantly reduces computational time in the design process The problem of selection of optimal parameters of fuzzy logic power system stabilizer is converted into an optimization problem and which is solved by genetic algorithm with the integral of squared time squared error (ISTSE) based objective function To demonstrate the robustness of the proposed genetic based fuzzy logic power system stabilizer, simulation studies on multimachine system subjected to small perturbation and three-phase fault have been carried out Simulation results show the superiority and robustness of GA based fuzzy logic power system stabilizer as compare to conventionally tuned controller

http://www.wseas.us/e-library/conferences/2011/Montreux/MECHICSE/MECHICSE-35.pdf

Design of Genetic Algorithm Based Fuzzy Logic Power System Stabilizers in Multimachine Power System

Power System Stabilizers (PSSs) are the most well-known and efficient devices to damp the power system oscillations caused by interruptions. This paper introduces a novel algorithm to determine the PSS parameters, using the multi-objective optimization approach called particle swarm optimization with the passive congregation (PSOPC). The tuning of the PSS parameters is usually formulated as the objective function with constraints, including the damping ratio and damping factor. Maximization of the damping factor and the damping ratio of power system modes are taken as the goals or two objective functions, when designing the PSS parameters. The optimization procedure handles the problem-specific constraints using a penalty function. This could enhance the diversity of the swarm and lead to a better outcome. The two-area multi-machine power system, under a wide range of system configurations and operation conditions is investigated, to illustrate the performance of the proposed approach. In this paper, the performance of the proposed PSOPC is compared to the Standard Particle Swarm Optimization (SPSO) and Genetic Algorithm (GA) in terms of parameter accuracy and computational time. The results verify that, the PSOPC is a much better optimization technique, in terms of accuracy and convergence, compared to PSO and GA. Furthermore, nonlinear simulation and eigenvalue analysis based results also confirm the efficiency of the proposed technique.

https://academicjournals.org/article/article1380881994_Eslami et al.pdf

Tuning of power system stabilizers using particle swarm optimization with passive congregation

This study proposed a novel algorithm to tune and coordinate power system stabilizers (PSSs) in multi-machine power systems. For a multi-machine power system, the coordination of the PSS parameters is generally formulated as an objective function with constraints including the damping ratio and damping factor. A novel hybrid particle swarm optimization (PSO) with the passive congregation algorithm, called PSOPC, is applied in this work to design a robust power system stabilizer. The PSOPC is modified from PSO by adding the passive congregation model. This could enhance the diversity of the swarm and lead to a better outcome. The two area multi-machine power system, under a wide range of system configurations and operation conditions, are studied to illustrate the performance of the proposed algorithm with the multi-objective function. Nonlinear simulation and eigenvalue analysis results confirm the efficiency of the proposed technique. The results illustrate that the PSOPC has an advanced in terms of accuracy and convergences. Index Terms -Multi-objective optimization, Design PSS, Dynamic Stability, Particle Swarm Optimization.

Optimal Tuning of Power System Stabilizers Using Modified Particle Swarm Optimization

A real coded genetic algorithm based PID controller (RCGAPID) is presented in this paper to improve power system dynamic stability. The proposed RCGAPID parameters are optimized using real coded genetic algorithm (RCGA). The design problem of the proposed controller is formulated as an optimization problem and RCGA is employed to search for optimal PID controller parameters. Integral absolute error (IAE) and integral squared error (ISE) performance indexes have been used for the determination of the PID controller parameters in power systems stabilization. The performance of the proposed RCGAPID under various loading and small disturbance conditions are analyzed and compared in single machine infinite bus (SMIB) power system. Simulation results show the effectiveness and robustness of proposed controllers to improve the stability performance of power system. Copyright © 2010 Praise Worthy Prize S.r.l. - All rights reserved.

Robust Design of PID Controller for Power System Stabilization by Using Real Coded Genetic Algorithm

This paper presents the simultaneous tuning of power system stabilizers in a multi-machine power system. The problem of selecting the parameters of power system stabilizers in converted into an optimization problem that is solved by genetic algorithm using eigen value based objective function. The dynamic performance of the system has been investigated under small perturbation and large disturbance. The performance of genetic algorithm based PSS has been compared with the conventional power system stabilizer. The non-linear simulation results on 4 machine, 11 bus system verifies the effectiveness of the proposed genetic algorithm based power system stabilizers

http://www.wseas.us/e-library/conferences/2006tenerife/papers/541-248.pdf

Simultaneous Stabilization of Multimachine Power System Using Genetic Algorithm Based Power System Stabilizers

This paper presents a systematic approach for the design of power system stabilizer using genetic algorithm and investigates the robustness of the GA based PSS. The proposed approach employs GA search for optimal setting of PSS parameters. The performance of the proposed GPSS under small and large disturbances, loading conditions and system parameters is tested. The eigenvalue analysis and nonlinear simulation results show the effectiveness of the GPSS to damp out the system oscillations. It is found tat the dynamic performance with the GPSS shows improved results, over conventionally tuned PSS over a wide range of operating conditions. Keywords—Genetic Algorithm, Genetic power system stabilizer, Power system stabilizer, Small signal stability

Manisha Dubey

Papers

Design of Genetic-Algorithm Based Robust Power System Stabilizer

Genetic Algorithm based Tuning of Fuzzy Logic Damping Controller for Multimachine Power System.