摘要
针对光伏软最大功率跟踪算法(MPPT)中BP神经网络易陷入局部最优解,模糊控制动态性能差等不足,提出一种BP神经网络-自适应模糊控制的光伏MPPT算法(BP-FLC)。BP神经网络利用光照强度和温度直接预测最大功率点参考电压,将该参考电压与光伏电池电压的差值和前一时刻占空比D(n-1)作为模糊控制的输入,利用收缩因子对D(n-1)进行优化直接调节Boost电路占空比D(n),实现最大功率点平稳跟踪。利用MATLAB/Simulink进行仿真验证,实验结果表明,提出的算法与自适应扰动观测法、模糊控制和粒子群算法相比,具有良好的跟踪性能和效率。
Since BP neural network is easy to fall into local optimal solution and the poor dynamic performance of fuzzy control in photovoltaic soft MPPT algorithm,the BP neural network and adaptive fuzzy control photovoltaic MPPT algorithm(BP-FLC)is proposed.The deviation between the reference voltage and the photovoltaic cell voltage and the duty ratio D(n-1)at the previous time are used as the inputs of the fuzzy control.The constriction factor is employed to optimize D(n-1),and the duty cycle D(n)of the Boost circuit is controlled to achieve MPPT.Simulations on MATLAB and experimental verification are carried out under atmospheric conditions,the results show that the proposed algorithm has better tracking speed and efficiency than the adaptive perturbation and observation,fuzzy control and particle swarm optimization algorithm.
作者
张严
王亚君
余佳琪
Zhang Yan;Wang Yajun;Yu Jiaqi(School of Electronic and Information Engineering,Liaoning University of Technology,Jinzhou 121001,China)
出处
《国外电子测量技术》
北大核心
2022年第4期62-69,共8页
Foreign Electronic Measurement Technology
基金
辽宁省自然科学基金(2020-MS-291)项目资助
