Modeling and control of a photovoltaic-wind hybrid microgrid system using GA-ANFIS

This paper aims to model a PV-Wind hybrid microgrid that incorporates a Battery Energy Storage System (BESS) and design a Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) controller to regulate its voltage amid power generation variations. Two microgrid models have been developed; a scalable Simulink Case Study Model from underlying mathematical equations and a nested voltage-current loop-based Transfer Function model. The proposed GA-ANFIS controller has been used as a Maximum Power Point Tracking (MPPT) algorithm to optimize the converter outputs and provide voltage regulation. The performance of the GA-ANFIS algorithm was compared with the Search Space Restricted-Perturb and Observe (SSR-P&O) and the Proportional-plus-Integral-plus-Derivative (PID) controllers using a simulation model built in MATLAB/SIMULINK. Results indicated that the GA-ANFIS controller is superior to the SSR-P&O and PID in terms of reduced rise time, settling time, overshoot, and the ability to handle non-linearities in the microgrid. In future work, the GA-ANFIS microgrid control system can be replaced with a three-term hybrid artificial intelligence algorithms controller.