Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for AC Microgrid

Microgrids (MGs) have been widely implemented as they increase the efficiency and resiliency of electrical networks. However, the uncertain nature of renewable energy resources (RERs) integrated into the MGs usually results in different technical problems. System stability, the most challenging problem, can be achieved via a robust power management strategy (PMS) of the MG. This paper introduces a PMS based on adaptive neuro fuzzy inference system (ANFIS) for AC MG consisting of a diesel generator (DG), a double fed induction generator (DFIG) driven by a wind turbine (WT) and a solar photovoltaic (PV) panel. The proposed strategy aims to achieve MG power balance, decrease DG fossil fuel to minimum consumption, keep the MG voltage stability and finally tracking the maximum power point (MPP) of each RER. Metaheuristic optimization techniques; including genetic algorithm (GA) and particle swarm optimization (PSO), are employed to train the ANFIS to accomplish the desired objectives and fulfill the generation/consumption balance. The proposed AC MG with the PMS is simulated by the MATLAB/Simulink software in order to analyze the system performance under different climatic conditions. The simulation results under symmetrical and asymmetrical electrical faults validated the effectiveness of the proposed strategy.