Shadows falling on photovoltaic (PV) modules result in partial shading conditions (PSCs). These conditions affect the power generation of a PV system because of their varying nature. As a result of PSCs, multiple peaks are created; therefore, it is important to identify the global maximum power point (GMPP) for optimal output power. Several maximum power point tracking (MPPT) techniques have been proposed in the literature; however, they face challenges such as oscillation at steady state, long convergence time, high complexity, and low accuracy. In this study, an improved musical chairs algorithm with local adaptive exploration is proposed for MPPT of PV systems under partial shading conditions. The proposed method combines the population-based exploration capability of the musical chairs algorithm with a localized duty-cycle adjustment mechanism around the best operating point. Unlike an offline exhaustive scan, the proposed local exploration stage uses only a small set of neighboring duty-cycle candidates, making the method more suitable for online MPPT implementation. The results are analyzed using the MATLAB/Simulink tool for a 4 × 4 PV array under PSCs. The IMCA-LAE algorithm is compared against the perturb and observe (P&O) algorithm, the incremental conductance (INC) algorithm, the musical chairs algorithm (MCA), and the gray wolf and whale optimization algorithm (GWWA) to illustrate the effectiveness of the suggested hybrid MPPT approach. The efficacy is further examined regarding five performance criteria: generated output power, convergence time, mismatch power loss, efficiency, and fill factor. The proposed IMCA-LAE outperformed the other algorithms.
Ishaya et al. (Tue,) studied this question.