Application of Evolutionary Optimization Techniques for PSS Tuning

Abstract

In this chapter, bacteria foraging optimization (BFO) and chaotic ant swarm optimization (CASO) are individually considered to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. A comparative performance study of these four variants of PSSs is also made. It is revealed by applying either BFO or CASO that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. A comparison between the results of the BFO and that of genetic algorithm (GA) is conducted in this study. The comparison reveals that BFO is more effective than GA in finding the optimal transient performance. CASO explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. For on-line, offnominal operating conditions Sugeno fuzzy logic (SFL) based approach is adopted. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.KeywordsAnt colony optimizationbacteria foraging optimizationchaotic ant swarm optimizationgenetic algorithmparticle swarm optimizationpower system stabilizerSugeno fuzzy logicswarm intelligence