Performance of Hybrid Fuzzy and Neuro-fuzzy Controller with MOHGA based MPPT for a Solar PV Module on Various Weather Conditions

Abstract

This paper presents the integration of fuzzy logic (FL) and neuro-fuzzy (NF) with genetic algorithm (GA) to propose FL controller (FLC-GA) and NF controller (NFC-GA) based on Maximum Power Point Tracking (MPPT) for the solar photovoltaic (PV) module. Multi-Objective Hierarchical GA (MOHGA) is used to extract the fuzzy rules and simultaneously fine-tuned the shape of membership functions and the system parameters. In the simulation, the current-voltage characteristic from the PV equivalent circuit model is formulated from the neural networks estimation model in order to calculate the referenced MPP at various weather conditions. It is shown that the FLC-GA performs the best stabilized accuracy at the steady state over the conventional FLC, the NFC-GA, the conventional incremental conductance (IC) method and the perturb and observe (P&O) method respectively. However, for the case of the time response at the transient state, the NFC-GA performs the fast tracking to the MPP over the conventional FLC, the FLC-GA, the P&O method, and the IC method respectively. Furthermore, the high complex structure of NFC-GA including many system parameters which may lead ineffective controller is optimized through MOHGA. The optimized NFC-GA is considered to perform the best result both transient and steady state except for the existing of the severe overshoot which is not suitable for the practice.