Dynamic Reconfiguration Method of Photovoltaic Array Based on Improved HPSO Combined with Coefficient of Variation

Abstract

In order to address the issue of power loss resulting from partial shadow and enhance the efficiency of photovoltaic power generation, the photovoltaic array reconfiguration technology is being increasingly utilized in photovoltaic power generation systems. This paper proposes a reconfiguration method based on improved hybrid particle swarm optimization (HPSO) for the photovoltaic array of TCT (total-cross-tied) structure. The motivation behind this method is to get the best reconfiguration scheme in a simple and efficient manner. The ultimate goal is to enhance the output power of the array, save energy, and improve its overall efficiency. The improved HPSO introduces the concept of hybridization in genetic algorithms and adopts a nonlinear decreasing weight method to balance the local search and global search ability of the algorithm and prevent it from falling into the local optimal solution. The objective function used is the variation coefficient of the row current without the weight factor. This approach saves time and balances the row current of the array by altering the electrical connection of the component. In the 4 × 3 array, the improved HPSO is compared with the Zig-Zag method. In the 9 × 9 array, the improved HPSO is compared with the CS (competence square) method and the improved SuDoKu method. The simulation results show that the power enhancement percentage of the improved HPSO is between 6.39% and 28.26%, and the power curve tends to single peak characteristics. The improved HPSO has a smaller mismatch loss and a higher fill factor in the five shadow modes, which can effectively improve the output power, and it is convenient to track the maximum power point later.