Optimal Rule-Base in Multi-Machine Fuzzy PSS Using Genetic Algorithm

This paper presents a Genetic Algorithms (GA) based rule generation method for Fuzzy Power System Stabilizer (FPSS) to enhance damping of the power system low frequency oscillations. This proposed controller is more efficient because it cope with oscillations and different operating points. There is no doubt that fuzzy controller is tuned on line from the knowledge base and fuzzy interference. Therefore, in this paper for achieving the acceptable level of robust performance exact tuning of fuzzy rule base are very important. For this purpose, the rules of Fuzzy PID controller will be tuned by GA which can reduce fuzzy effort and taking large parametric uncertainties in to account. Also this newly proposed technique make a flexible controller in different operating points. This controller will be applied on 3 machine 9 buses standard power system with different operating conditions in present of disturbance and nonlinearity. The efficacy of proposed controller is compared with robust PSS that tune using Particle Swarm Optimization (RPSSPSO) through FD and ITAE performance indices. According to results, this is cleared that the proposed method of tuning the fuzzy controller’s rules is an attractive alternative to conventional fixed gain stabilizer design as it retains the simplicity of the conventional PSS and still guarantees a robust acceptable performance over a wide range of operating and system condition.