Natural Genetics Adapted Control for an Autonomous Wind-Battery Based Microgrid

This paper proposes a natural genetics and evolution based control strategy for adjusting the gains of the proportional and integral (PI) controller meant for regulating the speed of the generator of an autonomous microgrid system using wind energy generation and a battery energy storage (BES). The variable wind speed operation leads to the dereliction of the fixed gain PI controller as it is sensitive to parameter variations and load disturbances. The genetic algorithm (GA) is implemented for adapting the gains of PI controller according to the changing operating conditions. It avoids the entrapments of the optimal solution in the local search space. The optimal solution is selected on the basis of best fit candidate. The synchronous generator (SG) reference speed is obtained using perturb and observe (P&O) maximum power extraction scheme. The flexible power flow control is obtained using a BES with a bidirectional converter. A prototype of the microgrid is developed in the laboratory and tested in accordance to the IEEE-519 standard under steady state and dynamic conditions including variable wind speeds and load removal.