Modified Grey Wolf Optimization for Maximum Power Point Tracking in Photovoltaic System under Partial Shading Conditions

Abstract

Maximum power point tracking (MPPT) is a method often used to maximize photovoltaic (PV) power output. In this paper, a modified gray wolf optimization (MGWO) is proposed to improve MPPT capability for PV in partial shade conditions. In the gray wolf optimization standard (GWO), it cannot maintain a balance between exploration of global optimization parameters and exploitation of local optimization parameters, which results in inaccurate optimal results. Therefore, the GWO is modified by updating alpha every iteration to increase the convergence speed and avoid local optima trapped by partial shading conditions. The proposed algorithm is implemented by an interleaved boost converter and a low-cost microcontroller. Simulation and experimental results show that the proposed algorithm can improve tracking performance speed and accuracy. The contribution of this invention can be applied to intelligent electronic devices (IEDs) as maximum power tracking based on embedded controllers.