A Neuro-fuzzy Adaptive Power System Stabilizer Using Genetic Algorithms

This article presents the design technique of an adaptive power system stabilizer using adaptive neuro-fuzzy inference systems trained via data obtained from genetic algorithms. The parameters of a standard power system stabilizer are tuned using adaptive neuro-fuzzy inference systems to achieve a certain damping ratio and settling time at all load points within a wide region of operation. The overall transfer function of the system is derived in terms of the power system stabilizer parameters. A genetic algorithm is used to minimize a multi-objective optimization function that forces the damping ratio and settling time of the system to desired values. The optimization process is separately conducted at selected operating points to yield power system stabilizer parameters that change with load variations. Results of genetic algorithm optimization are used to form a training dataset of an adaptive neuro-fuzzy inference systems agent, which could give the power system stabilizer parameters at any load within the specified region of operation. Results of power system stabilizer testing show that the desired performance indices could be fulfilled from light load to over load under both lagging and leading power factor conditions. System performance shows a remarkable improvement of dynamic stability by obtaining a well-damped time response.