A grasshopper optimization algorithm aided maximum power point tracking for partially shaded photovoltaic systems

Photovoltaic (PV) systems exhibit non-linear output power voltage (P-V) characteristics for varying input irradiation and temperature. Due to this, the Maximum Power Point Tracker (MPPT), a widely used optimization technique is indispensable in PV power systems. This nonlinearity gets more complicated when PV panels experience partial shading in an array. During partial shading, the panels receive non-uniform irradiance and, due to this the current-voltage (IV) curve is multimodal and so are P-V curves. Therefore, PV array which is partially shaded will exhibit multiple power peaks which are termed global maxima and local maxima. The most prevalent algorithms like Perturb and Observe (P&O) and Incremental Conductance (INC) experiences a unique issue by getting stuck at local maxima and not grasping the global power peak. This research article proposes a new Grasshopper Optimization Algorithm (GOA) which is capable of extracting maximum power even during terrible shading conditions. This algorithm is validated by comparing its performance with both conventional and other most prominent global search counterparts. The results demonstrate the effectiveness of the proposed algorithm.

Automated summary

Photovoltaic systems exhibit non-linear characteristics with varying input conditions, requiring the use of Maximum Power Point Tracker for optimization. In cases of partial shading, PV arrays may have multiple power peaks, leading to issues with traditional algorithms getting stuck at local maxima. A new optimization algorithm has been proposed in this study to extract maximum power efficiently.