The photovoltaic (PV) system contemplated in the study displays multiple peaks on power-voltage (P-V) curve under partial shading condition (PSC) results in a complicated maximum power point tracking (MPPT) process. Conventional MPPT algorithms work in an effective manner under uniform irradiance conditions. However, these algorithms are unable to track the global peak effectively under different irradiance conditions. In this study, a velocity of particle swarm optimisation-based Levy flight (VPSO-LF) for Global MPPT of PV system under PSCs is proposed. For the changes in irradiance, when verified with VPSO-LF, tracking time and a number of iterations are fewer to reach the global peak of PV array. It also minimises the number of tuning parameters of the velocity of particle swarm optimisation (PSO). The proposed technique is simulated in MATLAB/ SIMULINK as well as experimentally validated. It is observed that the results obtained using VPSO-LF is superior to conventional PSO and hill-climbing algorithm under different patterns of PV array. 2 PV system modelling For modelling and simulating PV system used a single-diode PV cell is shown in Fig. 1. It is implemented in MATLAB/SIMULINK environment based on the steps given in [31].

SummaryIn the study of photovoltaic (PV) system, power‐voltage (P‐V) curves exposed to view several peaks under partial shaded condition (PSC), which brings about muddled and most extreme maximum power point tracking (MPPT) process. Under uniform weather conditions, regular MPPT algorithms such as perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC) work in an effective manner. However, these conventional methods are unable to track global peak successfully under PSC. In this context, the evolutionary algorithms such as grey wolf optimization (GWO) perform better than conventional algorithms. However, the conventional GWO is not sufficient for exploration point of view to locate global best particles; and moreover, GWO deteriorates the convergence process. To overcome these drawbacks, a modified GWO (MGWO) is proposed in this paper to track global best particle, which improves the convergence process under static condition and as well as re‐initialization of parameters under dynamic conditions. The proposed method is verified using simulations as well as using experimental results. The obtained results demonstrate superiority compared to conventional GWO and HC methods under partial shaded patterns of PV array.

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