Achieving efficient optimal power extraction of centralized photovoltaic array by migranting whale algorithm under partial shading conditions

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Chinese explanation / 中文解读

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Original abstract

This paper proposes a novel bio-inspired optimization method named the migrating whale algorithm (MWA) for maximum power point tracking (MPPT) in photovoltaic (PV) systems under partial shading conditions (PSCs). Distinct from existing whale-inspired algorithms that mimic humpback whales’ bubble-net hunting behavior (e.g., whale optimization algorithm, WOA), MWA innovatively simulates their long-range cooperative migration behavior. This fundamental shift in biological metaphor leads to a unique “leader-calf” dual structure, where experienced leaders guide the pod and calves explore based on peer influence, a mechanism absent in WOA and its variants. This design intrinsically balances exploration and exploitation within a metaheuristic framework. To evaluate its performance, three case studies are conducted in MATLAB/Simulink: a startup test, a solar irradiance step change test, and a random irradiance variation test. The proposed MWA is compared against five existing MPPT algorithms—incremental conductance (INC), perturbation and observation (P&O), particle swarm optimization (PSO), grey wolf optimization (GWO), beluga whale optimization (BWO)—as well as four advanced metaheuristic methods: whale optimization algorithm (WOA), whale optimization algorithm-differential evolution (WOA-DE), whale optimization algorithm-particle swarm optimization (WOA-PSO), and jellyfish search (JS). Simulation results demonstrate that in the startup test, MWA achieves an energy harvest of 351.99 J, outperforming BWO (337.16 J), GWO (332.42 J), PSO (348.80 J), WOA (298.89 J), WOA-DE (329.56 J), WOA-PSO (334.22 J), JS (333.87 J), INC (231.48 J), and P&O (233.93 J). In the step change test, MWA yields 1913.50 J, which is 4.5% and 5.5% higher than BWO (1830.71 J) and GWO (1814.44 J), respectively, while maintaining the lowest average voltage deviation (0.51%). Under random irradiance variations, MWA generates 17658637.51 J (approximately 4.905 kWh), representing a 38.4% improvement over PSO (12762693.45 J), with a minimal average voltage deviation of 0.53%. Furthermore, MWA consistently achieves MPPT efficiencies above 99% across all operating scenarios, reaching 99.28%, 99.31%, and 99.23% under startup, step-change, and stochastic irradiance conditions, respectively. Comprehensive comparative analyses under dynamic and stochastic shading scenarios validate that MWA achieves superior tracking speed, steady-state stability, convergence accuracy, and energy harvesting efficiency for PV systems operating under complex PSC environments.

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