摘要
针对基于BP神经网络的光伏系统MPPT策略在光照强度突变时存在较大误差的问题,提出了一种改进的果蝇优化算法用于BP神经网络的权值和阈值优化,并建立了基于IFOA-BP神经网络算法的光伏系统MPPT控制的仿真模型。测试和仿真结果表明,IFOA的收敛速度和求解精度较改进前均有明显提升;IFOA优化后的BP神经网络收敛速度加快,预测误差减少;较之于电导增量法,IFOA-BP神经网络的MPPT策略在稳态条件下能明显抑制功率波动,在外界条件发生突变时,能迅速准确地追踪到最大功率点,具有良好的稳态精度和动态特性。
When the BP neural network is adopted to predict the voltage at the maximum power point,there is a big error if the light intensity changes drastically.Aiming at this problem,a novel improved fruit fly optimization algorithm(IFOA)determining the optimal BP neural network parameters(weight and threshold)is proposed,and a simulation model of the photovoltaic system MPPT control strategy based on the IFOA-BP neural network algorithm is established.The test and simulation results show that the IFOA has a great advantage in convergence speed and solution accuracy than FOA;IFOA-BP neural network can effectively increases the convergence speed and reduces the prediction error.Compared with the incremental conductance(INC)method,the proposed photovoltaic system MPPT control algorithm based on IFOA-BP neural network could suppress the oscillation around the maximum power point(MPP)under steady-state conditions and track down the MPP quickly and accurately when light intensity and temperature change drastically,which verifies the stability,precision and rapidity of the proposed MPPT method.
作者
闫超
倪福佳
刘嘉瑜
贺诗明
高振远
王少帅
Yan Chao;Ni Fujia;Liu Jiayu;He Shiming;Gao Zhenyuan;Wang Shaoshuai(Jiangsu Province Laboratory of Electrical and Automation Engineering for Coal Mining,China University of Mining&Technology,Xuzhou 221116,Jiangsu,China;School of Electrical and Power Engineering,China University of Mining&Technology,Xuzhou 221116,Jiangsu,China)
出处
《电测与仪表》
北大核心
2018年第8期24-29,130,共7页
Electrical Measurement & Instrumentation
基金
国家级大学生创新训练计划项目(201510290017)
国家自然科学基金资助项目(51504253)
江苏省自然科学基金项目(BK20161185)
关键词
光伏电池
最大功率点跟踪
BP神经网络
改进果蝇优化算法
photovoltaic cell
maximum power point tracking
BP neural network
improved fruit fly optimization algorithm
