A novel constraint‐based improved equilibrium optimization for global peak tracking of photovoltaic system under complex shading conditions

Summary The P–V characteristics of photovoltaic (PV) array exhibit multiple peaks during partial shading conditions (PSCs). Traditional maximum power point tracking (MPPT) approaches are unable to locate the global power point and frequently converge to the local power point. In recent years, evolutionary techniques have given a satisfactory result by tracking global maximum power point (GMPP) but at the cost of ample memory storage and computational burden. Therefore, this paper proposes an improved equilibrium optimization (IEO) technique to track maximum power from the solar photovoltaic system under PSC. The reduced search space exploration by using the skipping method is implemented to improve the convergence speed. The proposed IEO algorithm is capable of differentiating between uniform shading, different PSCs, and solar intensity variation with fast tracking speed. Similarly, the proposed algorithm has been compared with deterministic Jaya (DM‐Jaya), crow search algorithm (CSA), and equilibrium optimization (EO) algorithms. The effectiveness of the proposed IEO technique‐based MPPT has been validated through simulations and experiments. The results demonstrate the capability of IEO in tracking GMPP with an average efficiency of 99.72% and average tracking time of 0.708 s. Further, the proposed algorithm has been implemented for grid integration under PSC. The results reveal that the proposed method has high PV tracking efficiency, negligible steady‐state error, a low MPPT period, and a quick convergence velocity.