Partial shading conditions remain a significant challenge for centralized photovoltaic arrays, causing power loss and reduced efficiency. Researchers have developed the migrating whale algorithm (MWA), a new optimization technique designed specifically to enhance maximum power point tracking (MPPT) performance under these difficult operating conditions.
Unlike existing whale-based algorithms that simulate hunting behavior, MWA draws inspiration from the long-range cooperative migration patterns of whale pods. The method employs a distinctive leader-calf dual structure where experienced leaders guide exploration while younger members learn through peer interaction. This design naturally balances the exploration and exploitation trade-off inherent to optimization problems.
Comprehensive simulation testing in MATLAB/Simulink compared MWA against nine competing algorithms including particle swarm optimization, grey wolf optimization, and several whale-inspired variants. Under startup conditions, MWA harvested 351.99 joules, significantly outperforming the second-best performer. During step-change tests simulating rapid irradiance shifts, the algorithm achieved 1913.50 joules with the lowest voltage deviation at 0.51 percent. Most impressively, under random irradiance variations representative of real-world cloud cover patterns, MWA generated approximately 4.9 kilowatt-hours per test cycle—a 38.4 percent improvement over particle swarm optimization.
Across all test scenarios, MWA maintained MPPT efficiency above 99 percent, reaching 99.28 percent during startup, 99.31 percent during step changes, and 99.23 percent under stochastic conditions. The algorithm demonstrated superior tracking speed, steady-state stability, and convergence accuracy compared to conventional approaches like perturbation-and-observation and incremental conductance methods.
These results suggest MWA could substantially improve energy harvesting in real-world solar installations where partial shading from clouds, trees, or adjacent structures frequently occurs. The technique may enable more efficient operation of distributed photovoltaic systems and reduce the need for complex hardware-based shade mitigation strategies. Further validation through hardware implementation and field testing would be necessary before practical deployment.
