基于改进粒子群算法的光伏最大功率点跟踪

Photovoltaic Maximum Power Point Tracking Based on Improved Particle Swarm Optimization Algorithm

光伏发电具有间歇性和不确定性,且光伏电池输出特性在很大程度上受辐照度和温度影响,为提高光伏发电效率与系统稳定性,需要最大功率跟踪(Maximum Power Point Tracking,MPPT)技术使光伏阵列输出功率稳定在设定值。光伏系统实际环境复杂多变,在局部阴影条件下,传统MPPT控制不能有效跟踪最大功率点(Maximum Power Point,MPP)。在分析光伏电池输出特性基础上,提出基于改进粒子群算法(Particle Swarm Optimization,PSO)的最大功率跟踪方案,动态改变惯性权重大小,实现对最大功率点的最优跟踪。将传统MPPT算法与基于改进PSO的MPPT控制仿真结果对比,验证改进粒子群算法在复杂环境下跟踪最大功率的优越性,结果表明,改进粒子群算法的光伏系统输出功率跟踪速度与精度均优于传统MPPT算法,对于提高光伏电池效率具有极大的现实意义。

Photovoltaic power generation is intermittent and uncertain,and the output characteristics of photovoltaic cells are largely affected by irradiance and temperature. In order to improve the efficiency of photovoltaic power generation and system stability,it is necessary to stabilize the output power of photovoltaic array at the set value through maximum power point tracking(MPPT)technology.However,the actual environment is complex and changeable.Under partial shadow conditions,the traditional MPPT control cannot effectively track the maximum power point(MPP).Based on the analysis of the output characteristics of photovoltaic cells,a MPPT algorithm based on improved particle swarm optimization(PSO)was proposed to dynamically change the inertia weight and realize the optimal tracking of the MPP.The simulation results of traditional MPPT algorithm and MPPT control based on improved PSO were compared to verify the superiority of improved PSO algorithm in MPPT under complex environment.The simulation results show that the PSO photovoltaic system output power tracking speed and accuracy are better than traditional MPPT algorithm,which have great practical significance for improving the efficiency of photovoltaic cells.