A Comparison Study on Intelligent Control Strategies of Power System Stabilizers

Abstract

Power System Stabilizers(PSSs) are well-designed devices to measure and enforce improvements in synchronous generators’ system-stability, which offer overwhelmingly superior cost performance compared to other optimal reconstruction or enhancement of power systems. The techniques of PSSs have been focused by power industry and academic circles in many years. The paper presents a performance comparison of several advanced techniques based on Adaptive Fuzzy Control, Artificial Neural Network (ANN), Genetic Algorithm(GA) and Hybrid Artificial Intelligent(HAI), Fuzzy Logic and Particle Swarm Optimization(FLPSO) techniques. With their merits on dealing with PSSs’ implemental structures, models with unknown or variable parameters, we study the main indices to compare the performance of the referred intelligent techniques including simplicity of prototype, robustness and response speed, complexity of algorithm, flexibility in implementation and applicability to hybrid AVRs so on. The comparison results show that intelligent techniques improve PSSs comprehensive performance of being more effective and vigorous in damping out low frequency oscillations by overcoming inherent limitations in conventional control methodologies. Intelligent techniques could be especially considered in application of smart grid with large-scale grid-connected renewable energy power and random high power loads.