Yibo Wang, Haitao Wu, Rongxin Qiu
The optimal working condition of photovoltaic (PV) array will change for the illumination distribution. The power-voltage (P-V) curve will has several maximum power points (MPP) when the photovoltaic array working under partially shaded condition (PSC). Researchers has applied the particle swarm optimization (PSO) algorithm in maximum power point tracking (MPPT) when PV array working under PSCs. However, in PSO cause the intelligent agents’ moving speed is constant, the convergence speed could not meet the need when the PV array working condition change rapidly; and for the social learning factor of PSO is constant and equal for every agent, if there are more agents fall into local optimum point, these agents cannot jump out from local optimum point cause every agent just could gather together rather than searching for the point has better value. In order to improve these problems of PSO, this paper proposed multi-hierarchy second-order oscillation particle swarm optimization (MHSOPSO) algorithm which combine the second-order evolution equation and analytic hierarchy process (AHP) principle to improve the convergence speed and look beyond the local optimum ability. The model is built in MATLAB and simulated by PSO, second-order oscillation particle swarm optimization(SOPSO) and the proposed MHSOPSO under different working conditions. The result shows that the MHSOPSO could control the PV array working at a higher power under different PSCs in shorter time, in different working conditions, MHSOPSO is able to achieve Global Maximum Power Point (GMPPT) control within 0.5s.
partial shadow; photovoltaic array; second-order oscillation evolutionary equation; AHP; PSO
Cite this paper
Yibo Wang, Haitao Wu, Rongxin Qiu. (2022) Maximum Power Point Tracking of Photovoltaic Array based on Multi-hierarchy Second-order Oscillation Particle Swarm Optimization Algorithm. International Journal of Renewable Energy Sources, 7, 17-25