A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids

As the solar PV system (SPVS) suffered from an unavoidable complication that it has nonlinearity in I-V curves, the optimum maximum power point (MPP) measurement is difficult under fluctuating climatic conditions. For maximizing SPVS output power, MPP tracking (MPPT) controllers are used. In this paper, a new adaptive fuzzy logic controller (AFLC) based MPPT technique is proposed. In this proposed AFLC, the membership functions (MFs) are optimized using the Grey Wolf Optimization (GWO) technique to generate the optimal duty cycle for MPPT. Four shading patterns are used to experiment with the performance of the proposed AFLC. The proposed approach tracks the global MPP for all shading conditions and also enhances the tracking speed and tracking efficiency with reduced oscillations. The effectiveness and robustness of proposed AFLC based tracker results over P&O and FLC are validated using Matlab/Simulink environment. The proposed AFLC overcome the drawbacks of the classical P&O, and FLC approaches. (c) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposed a new adaptive fuzzy logic controller (AFLC) based MPPT technique, optimizing the membership functions to achieve the optimal duty cycle for MPPT, improving tracking speed and efficiency, and tracking the global MPP under various shading conditions.