Robust fuzzy logic power system stabilizer based on evolution and learning

A robust fuzzy logic power system stabilizer (FLPSS) based on evolution and learning is proposed in this paper. A hybrid algorithm that combines learning and evolution is developed whereby each one complements other's strength. Parameters of FLPSS are encoded in chromosome (individual) of genetic algorithm (GA) population. Population of FLPSS in GA learns to stabilize electromechanical oscillations in power system at an operating point, as the best fitness becomes large steady value during successive generations. Operating region of FLPSS is enlarged by learning more operating points over the operating domain. Best FLPSS drawn from last generation is saved as designed FLPSS. Effectiveness of the proposed method is validated on a single machine infinite bus (SMIB) power system. Promising optimal stabilizing performance with designed FLPSS for considered power system is obtained at wide range of operating points.