THE ENHANCEMENT OF POWER SYSTEM STABILIZER VIA ARTIFICIAL INTELLIGENCE HYBRID SYSTEMS

Abstract

Three Artificial Intelligence (AI) based hybrid Power System Stabilizers (PSSs) have been designed and their effectiveness demonstrated. The techniques used are Fuzzy Knowledge Based Controller (FKBC), Tabu Search (TS) and Genetic Algorithm (GA). The FKBC has been developed to perform the function of a power system stabilizer and to provide a supplementary signal to the excitation system of the synchronous generator. The method used for storing and representing the fuzzy rules is called the Fuzzy Associative Memory (FAM) matrix. The well-defined FAM determines the performance of the FKBC. TS and GA are thus implemented to determine the construction and optimization of the FAM. In addition, a Strict-Tabu (S-Tabu) optimization of PSS parameters has also been developed. To achieve good damping characteristics over a wide range of operating conditions, S-Tabu is used to optimize PSS. Those controllers have been tested in Single Machine Infinite Bus (SMIB) and multimachine systems for various types of disturbance. To highlight the effectiveness of the developed controllers, comparisons with the Conventional PSS (CPSS) are presented.