Neural network tuned fuzzy logic power system stabilizer design for SMIB

Abstract

Steadiness of power system is a significant issue in power system operation. In this article design of Neural Network tuned Fuzzy logic power system stabilizer (NNTFLPSS) for single machine infinite bus (SMIB) system is proposed to settle down low frequency swinging that improves small signal stability in power system. The speed deviance and variation in speed deviance of the rotor of synchronous generator from the trained neural network were considered as the feedback to the fuzzy logic power system stabilizer (FLPSS) to recover the power system from small signal stability problem by refining damping oscillations. The comparative reading was noted for rotor speed deviances and rotor angle deviances using conventional PSS (CPSS), Fuzzy logic based power system stabilizer (FLPSS) and NNTFLPSS. The MATLAB simulation results obtained indicates the improved performance of NNTFLPSS over the CPSS and FLPSS.