一种光伏发电多峰值MPPT控制算法研究

Research on a multi-peak MPPT control algorithm for photovoltaic power generation

当光伏阵列被周围建筑或者房屋等部分遮挡时,其输出的功率-电压曲线上出现多个功率峰值。此时,经典的最大功率点跟踪(MPPT)算法存在追踪效率低的问题。提出一种基于粒子群优化(PSO)结合电导增量法(INC)的复合MPPT算法,在算法中引入自适应学习因子、惯性因子和淘汰机制,用以提高追踪精度和收敛速度,再结合INC进一步快速找到最大功率点。仿真结果表明,所提出的复合算法在均匀辐照度和局部遮光下都能快速而准确地跟踪最大功率点。传统PSO算法与灰狼优化(GWO)算法相比,所提算法的收敛时间更短,且稳态功率振荡更小,有效提高了光伏系统的发电效率。

When a photovoltaic array is partially shaded by surrounding buildings or houses,multiple power peaks appear on its output power-voltage curve.In this case,the classical maximum power point tracking(MPPT)methods face the problem of low tracking efficiency.So a composite MPPT algorithm based on improved particle swarm optimization(PSO)combined with incremental conductance method(INC)is proposed.The adaptive learning factor,inertia factor and elimination mechanism are introduced into the algorithm to improve tracking accuracy convergence speed.Finally,the maximum power point is quickly found with INC.The simulation results show that the proposed composite algorithm can track the maximum power point quickly and accurately under both uniform irradiance and partial shading.Compared with the traditional PSO and grey wolf optimization(GWO)algorithm,the proposed algorithm has shorter convergence time and smaller steady-state power oscillation,which effectively improves the power generation efficiency of the photovoltaic system.