模糊RBF太阳电池最大功率点跟踪算法  

Maximum power point of solar cells tracking algorithm based on fuzzy RBF

作　　者：袁娇 李涛 李文全 贺乔卿 李宸宇 马昊翔 YUAN Jiao;LI Tao;LI Wenquan;HE Qiaoqing;LI Chenyu;MA Haoxiang(School of Physics and Electronic Information Engineering,Qinghai Normal University,Xining 810008,China)

机构地区：[1]青海师范大学物理与电子信息工程学院

出　　处：《传感器与微系统》2020年第1期154-156,160,共4页Transducer and Microsystem Technologies

基　　金：教育部“春晖计划”合作科研项目(Z2016107)

摘　　要：为了有效地利用太阳能,提高光伏发电系统的效率,通过分析太阳电池输出特性和最大功率点跟踪原理,结合模糊控制算法、神经网络与比例-积分-微分(PID)算法的优势,提出一种模糊径向基函数(RBF)神经网络的太阳电池最大功率点跟踪算法。利用模糊RBF神经网络对PID控制器的控制参数进行自适应整定,从而精确调节光伏电池变换器的功率开关占空比,达到太阳电池最大功率点跟踪控制。仿真与实测结果表明:算法能有效提高系统的响应速度、自适应能力。In order to effectively use solar energy,improve the efficiency of photovoltaic(PV)systems,by analyzing the output characteristics of solar cells and the principle of maximum power point tracking,and combining the advantages of fuzzy control algorithm,neural network and proportion integration differentiation(PID)algorithm,a fuzzy radial basis function(RBF)neural network tracking algorithm for maximum power point of solar cells is proposed.The fuzzy-RBF neural network is used to adaptively adjust the control parameters of the PID controller to adjust the duty ratio of the power switch to achieve the maximum power point tracking control of solar cells.Simulation and experimental results show that the algorithm can effectively improve response speed and adaptive capacity of the system.

关 键 词：光伏系统 模糊径向基函数神经网络 比例-积分-微分(PID)控制 最大功率点跟踪 

分 类 号：TM615[电气工程—电力系统及自动化] TP305[自动化与计算机技术—计算机系统结构]