Volume 17, No 2, 2020
Enhancement in PI Parameter Prediction Using Segmented Mutation based Genetic Algorithm
Faisal G. Beshaw and Intisar K. Saleh
This paper present the improvement in integration of electrical grid and induction generator with self-excited mode. This induction generators are mostly used in wind turbine. For this purpose static synchronous compensator is adapted with direct adaptive strategy with some adjustable controller parameters and an adjusting mechanism to adjust them using model reference adaptive control (MRAC). Model reference adaptive control is used to balance reactive power flow of this integration. Voltage-source inverter sinusoidal pulse width modulation handles the different operational condition in wind energy system. According to that ability of staying connected with grid in Brownout and blackout also increases. Because of occurrence of some faults at coupling of grid and generator, some abnormal operational condition generates. In proposed model, Genetic Algorithm with modified mutation function is used to tune proportional-integral controller. Instead of conventional random selection function for mutation, a modified algorithm is applied. For evaluation purpose Zafarana wind system integrated with Egyptian 220 networks is used. Results shows an improvement in performance of proposed model reference adaptive control using genetic algorithm which balances the current, voltage and speed of wind generator. It is also observed ability of staying connected with system under abnormal situation and during Brownout and blackout also increases with segmented mutation based genetic algorithm.
Keywords: Enhancement in Wind Energy Systems, PI Controller, Segmented Mutation based Genetic Algorithm, Power Generation, Renewable Resources.