Power Tracking Capability Enhancement of a Grid-Tied Partially Shaded Photovoltaic System Through MPC Based Maximum Power Point Technique

Abstract

Multiple studies in recent years have suggested various strategies for Maximum Power Point Tracking (MPPT) of the photovoltaic based distributed generation systems. When local shadowing occurs, the output–voltage–power curves of photovoltaic (PV) arrays display complicated multi-peak patterns. Because photovoltaic (PV) array characteristic curves have several local maxima, most standard tracking algorithms fail to detect maximum power point under partially shadowed situations. So numerous authors have discussed various MPPT methods to track the maximum power available during partial shading conditions. In this regard, this research article proposes a novel Model Predictive Controller (MPC) technique to track maximum power during non-uniform illumination conditions. The MPC technique is simple in method and easy in implementation. Further, the proposed approach features a quicker dynamic reaction and a better steady-state response. The proposed model is designed in Matlab/Simulink environment under partial shading conditions. For justifying the efficiency of the proposed controller, the proposed MPC controller characteristics have been compared with traditional methods such as Artificial Neural Network (ANN) and Fuzzy Logic Controller (FLC). A detailed comparison of the voltage, current and power values obtained from the MPC, ANN and FLC controller characteristics has been tabulated. The values obtained confers that the proposed controller is more efficient and the system dynamics are better in comparison to ANN and FLC methods thus justifying the real-time implementation of the proposed controller.