Partial shading remains one of the most persistent challenges in photovoltaic system design and operation. When clouds, trees, or buildings obstruct portions of a solar array, the mismatch between cells creates bottlenecks that can reduce total array output by 20–50% or more, far exceeding the percentage of shaded area. Traditional fixed-topology arrays cannot adapt to these dynamic shadow patterns, making power extraction highly suboptimal.
Dynamic reconfiguration—automatically rewiring array connections to optimize current and voltage distribution—offers a proven solution. By changing which modules are connected in series and parallel, operators can bypass shaded sections and maintain near-peak power output even under complex shading scenarios. However, identifying the best reconfiguration pattern in real time is computationally demanding, requiring rapid optimization across hundreds or thousands of possible configurations.
This research evaluated ten state-of-the-art metaheuristic algorithms—including genetic algorithms, particle swarm optimization, and newer variants—to determine which performs best for dynamic array reconfiguration. Testing covered both uniform shading and multiple partial-shading patterns on a 6×6 array architecture, a common configuration in utility-scale and commercial installations.
Key findings show that algorithm performance varies significantly depending on shading type and real-time constraints. Some algorithms excel at global optimization but require more computational time, while others deliver faster near-optimal solutions suitable for field implementation. The comparative data clarifies trade-offs between solution quality and execution speed—critical for microcontroller-based reconfiguration hardware deployed in the field.
These results enable solar developers and utilities to make informed choices when implementing adaptive reconfiguration systems. By matching algorithm capability to specific site conditions and hardware constraints, operators can unlock 5–15% additional annual energy yield in shaded environments, improving economic returns and grid stability.
