An effective dual-objective optimization to enhance power generation in a two-stage grid-tied PV system under partial shading conditions

Partial shading conditions (PSC) reduce the maximum output power from the photovoltaic (PV) array and introduce multiple peaks in the power–voltage curve. Therefore, the dynamic PV array reconfiguration approach is established as an effective method for obtaining irradiance equalization to maximize the output power under PSC. This paper proposes a dual-objective approach based on the spotted hyena optimization using a non-linear convergence factor (SHO-NCF) for (a) optimal reconfiguration of PV array and (b) maximum power point tracking with a minimum tracking period and negligible steady-state oscillations under PSC. A novel objective function is introduced to optimize the switching matrix to achieve irradiance equalization. To demonstrate the effectiveness of the proposed algorithm, simulations and experimental investigations have been performed. Further, the outcomes are compared with a traditional perturb and observe, particle swarm optimization, munkres algorithm, maximum–minimum tier equalization swapping method, and spotted hyena optimization. Performance analyses based on energy saving and income generation are performed to assess the efficacy of the proposed technique. Furthermore, the proposed algorithm is implemented for a single-phase, two-stage grid-connected PV system. Moreover, the maximum power generation increased to 18.166% by the proposed dual objective method compared to before reconfiguration.