Multicluster-based particle swarm optimization algorithm for photovoltaic maximum power point tracking

In this paper, a multi-cluster-based particle swarm optimization (MC-PSO) algorithm for photovoltaic (PV) maximum power point tracking (MPPT) is proposed to promote the MPPT performance in the partial shading condition. During the tracking process, each PV module is viewed as a particle and the PV modules with similar characteristics are put into the same cluster. The particles in the same cluster can refer the information to each other to realize the MPPT in the partial shading condition. In addition, multiple sampling points can be obtained at the same time to avoid the misjudgement problem during insolation changing rapidly. Thus, the tracking speed is also improved. The simulation results used by MATLAB is done and compared with the perturbation and observation (P&O) MPPT algorithm. The accuracy of MPPT of the proposed MC-PSO is improved to 96.3% in the partial shading condition. Finally, a 2.1kW prototype is implemented to verify the feasibility. The generated energy using the proposed method compared to the conventional P&O method is increased about 13.3%.