Novel Hybrid Mexican Axolotl Optimization with Fuzzy Logic for Maximum Power Point Tracker of Partially Shaded Photovoltaic Systems

Abstract

Due to the nonlinear relation between the generated power and voltage of photovoltaic (PV) arrays, there is a need to stimulate PV arrays to operate at maximum possible power. Maximum power can be tracked using the maximum power point tracker (MPPT). Due to the presence of several peaks on the power–voltage (P–V) characteristics of the shaded PV array, conventional MPPT such as hill climbing may show premature convergence, which can significantly reduce the generated power. Metaheuristic optimization algorithms (MOAs) have been used to avoid this problem. The main shortcomings of MOAs are the low convergence speed and the high ripples in the waveforms. Several strategies have been introduced to shorten the convergence time (CT) and improve the accuracy of convergence. The proposed technique sequentially uses a recent optimization algorithm called Mexican Axolotl Optimization (MAO) to capture the vicinity of the global peak of the P–V characteristics and move the control to a fuzzy logic controller (FLC) to accurately track the maximum power point. The proposed strategy extracts both the benefits of the MAO and FLC and avoids their limitations with the use of the high exploration involved in the MOA at the beginning of optimization and uses the fine accuracy of the FLC to fine-track the MPP. The results obtained from the proposed strategy show a substantial reduction in the CT and the highest accuracy of the global peak, which easily proves its superiority compared to other MPPT algorithms.