基于BSO的局部阴影下光伏最大功率点追踪

Photovoltaic Maximum Power Point Tracking Based on BSO Algorithm Under Partial Shading Condition

为了减小局部遮阴情况PSC(partial shading condition)下光伏系统的功率失配损失,提高最大功率点追踪MPPT(maximum power point tracking)的追踪速度和准确性,提出了基于天牛群优化BSO(beetle swarm optimiza?tion)算法的MPPT控制方法.把由天牛须搜索BAS(beetle antennae search)借鉴粒子群的群体优化思想而得到的BSO方法应用到MPPT控制,利用天牛的个体进化和群体学习等优势来提高MPPT的追踪速度和精确度.设置了多种光照情况来作仿真验证,并用粒子群方法进行比较分析.结果表明,所提的方法追踪速度更快、精确度更高,且追踪过程更稳定、功率波动较小.

To reduce the power mismatch loss of a photovoltaic(PV)system under the partial shading condition(PSC)and improve the tracking speed and accuracy of maximum power point tracking(MPPT),an MPPT control method is proposed based on the beetle swarm optimization(BSO)algorithm.The BSO algorithm,which is expanded from the beetle antennae search(BAS)inspired by the swarm optimization idea of particle swarm,is applied to the MPPT con⁃trol,and the beetle’s advantages such as individual evolution and group learning are used to improve the tracking speed and accuracy of MPPT.Several irradiation conditions are set to verify the novel method through simulations,and the particle swarm optimization(PSO)algorithm is used for comparison.Results show that the proposed method has faster tracking speed and higher accuracy.In addition,the tracking process is more stable and the fluctuations are of smaller magnitude.