Frequency Control of 5 kW Self-excited Induction Generator Using Gravitational Search Algorithm and Genetic Algorithm

For harnessing the wind energy, self-excited induction generator is becoming more popular in today’s scenario. Nonlinear loads lead to major drawbacks in self-excited induction generator such as poor voltage, frequency regulation and reactive power consumption. This poor voltage and frequency of SEIG depends on many factors like types of load, capacitance involved for reactive power compensation and prime mover speed. The improved performance of SEIG can be obtained by using steady-state analysis of equivalent circuit and usage of optimization techniques in SEIG machine. The main objective of this chapter to select the values of shunt and series capacitances at specified speed in order to achieve an optimum frequency regulation using gravitational search algorithm (GSA) and genetic algorithm (GA). GSA works on Newton’s law of gravity, whereas GA follows the steps of selection, crossover and mutation. Both the techniques are based on heuristic approach of gbest and pbest. Therefore, this study is carried out on an objective function of relative mean error of frequency regulation. Correspondingly, the minimum fitness is calculated for resistive and resistive-inductive load. The improved performance validates both the optimization techniques.