摘要
为了准确表征和预测光伏(PV)组件在不同工况下的电流电压(I-V)特性,提出一种利用改进Elman神经网络的光伏I-V曲线黑盒建模新方法.首先,通过皮尔森相关系数分析影响I-V曲线的环境因素;其次,使用基于电压电流的双线性插值法对实测I-V曲线进行重采样,以提高I-V曲线上数据点分布的均匀性;再次,使用基于辐照度温度的网格采样法对I-V曲线数据集进行下采样,降低数据冗余度,并利用量子粒子群(QPSO)算法优化Elman神经网络的初始权值和阈值,从而构造QPSO-Elman预测模型.最后,根据实测I-V曲线数据集进行实验验证和测试,并与多层感知机、未改进的Elman网络、支持向量机等算法进行对比.实验结果表明,所提出的建模预测方法精度更高,稳定性和泛化能力更好.
In order to accurately characterize and predict the current and voltage(I-V)characteristics of photovoltaic(PV)modules under different operating conditions,a new black box modeling method of PV I-V curve based on improved Elman neural network is proposed.Firstly,the environmental factors affecting the I-V curve are analyzed by PEARSON correlation coefficient;secondly,the bilinear interpolation method is used to resample the measured I-V curve to improve the uniformity of data points distribution on the I-V curve;then,the grid based sampling method is used to sample the I-V curve data set to reduce the data redundancy.Then,quantum particle swarm optimization(QPSO)is used to optimize the initial weights and thresholds of Elman neural network to construct the QPSO Elman prediction model.Finally,the experimental verification and test are carried out according to the measured I-V curve data set,and compared with the multi-layer perceptron,the unimproved Elman network,support vector machine and other algorithms.The experimental results show that the proposed prediction model has higher accuracy,better stability and generalization ability.
作者
罗林禄
陈志聪
吴丽君
林培杰
程树英
LUO Linlu;CHEN Zhicong;WU Lijun;LIN Peijie;CHENG Shuying(Institute of Micro-Nano Devices and Solar Cells,College of Physics and Information Engineering,Fuzhou University,Fuzhou,Fujian 350108,China)
出处
《福州大学学报（自然科学版）》
CAS
北大核心
2022年第2期198-205,共8页
Journal of Fuzhou University(Natural Science Edition)
基金
国家自然科学基金资助项目(61601127)
福建省自然科学基金资助项目(2021J01580)
福建省科技厅引导性基金资助项目(2019H0006)。
关键词
光伏阵列
I-V特性建模
QPSO算法
ELMAN神经网络
参数优化
photovoltaic(PV)array
I-V characteristic modeling
quantum particle swarm optimization(QPSO)algorithm
Elman neural network
parameter optimization
