福州大学学报（自然科学版）2017，Vol.45Issue(5)：652-658,7.DOI:10.7631/issn.1000-2243.2017.05.0652
一种PSO-SVM的光伏阵列故障检测与分类
Fault detection and classification for photovoltaic arrays based on PSO-SVM
摘要
Abstract
A fault detection and classification model for photovoltaic arrays is presented by using particle swarm optimization-support vector machine(PSO-SVM).The characteristic and faults of PV arrays are analyzed.Moreover,the appropriate feature vectors are selected and the normalized method is designed,respectively.In order to strengthen the accuracy of the proposed model,the RBF kernel function is applied to improve the model structure,whose parameters are optimized by the PSO algorithm.Based on the measured platform,the experiment data set of the PV array under normal working condition and eight types of faults are recorded.The data set are randomly divided into testing set and training set to train the PSO-SVM model.The accuracy of fault detection and fault classification are 99.89％ and 98.68％,respectively,which are superior to those of BP neural network and decision tree.关键词
光伏阵列/故障/检测/分类/粒子群优化/支持向量机Key words
photovoltaic arrays/fault/detection/classification/particle swarm optimization/support vector machine分类
信息技术与安全科学引用本文复制引用
林培杰,陈志聪,吴丽君,程树英..一种PSO-SVM的光伏阵列故障检测与分类[J].福州大学学报（自然科学版）,2017,45(5):652-658,7.基金项目
国家自然科学基金资助项目(61574038、61601127、51508105) （61574038、61601127、51508105）
福建省科技厅工业引导性重点资助项目(2015H0021、2015J05124、2016H6012) （2015H0021、2015J05124、2016H6012）
福建省教育厅科研资助项目(JAT160073) （JAT160073）
福建省经信委行业关键共性技术资助项目(83016006、830020) （83016006、830020）
