Genetic Algorithm-Based Fuzzy Controller for Improving the Dynamic Performance of Self-Excited Induction Generator

This paper introduces a new hybrid controller using artificial intelligence (AI) techniques to improve the dynamic performance of the self-excited induction generator "SEIG" driven by wind energy conversion scheme "WECS". The hybrid AI compromises a genetic algorithm (GA) and fuzzy logic controller (FLC). The role of the GA is to optimize the parameters of the fuzzy set to ensure a better dynamic performance of the overall system. The proposed controller is developed in two loops of the WECS scheme under study. The first loop is used to regulate the terminal voltage, by adjusting the self-excitation. This controller represents the reactive power control. In this case, the FLC will utilize the error and its change in terminal voltage to regulate the duty cycle of the capacitor bank. The second loop is used to adjust the mechanical power, by adapting the blade angle of WECS. Here, the FLC uses the frequency error and its change to adjust the blade angle of the wind turbine to control the active power input. The simulation results, which cover a wide range of electrical and mechanical disturbances, depict the effectiveness of the proposed controller compared with other AI techniques.