.png){kind=icon}
.png){kind=icon}
Return
Achieving efficient optimal power extraction of centralized photovoltaic array by migranting whale algorithm under partial shading conditions
.png){kind=icon}
OA
AI
Y
DOI：10.3389/fenrg.2026.1851528.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
solar energy
PV system
partial shading conditions
maximum power point tracking
migranting whale algorithm
Journal
IF:
2.4
Papers:
923
Citations:
1.4W
Organization
No organization information available
Cited Papers