Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network Optimized by Genetic Algorithm

作     者：Caixia Tao Xu Wang Fengyang Gao Min Wang Caixia Tao;Xu Wang;Fengyang Gao;Min Wang

作者机构：Department of Automation and Electrical EngineeringLanzhou Jiaotong UniversityLanzhou 730070China Ninghe Power Supply Co.Ltd.Tianjin 301500China 

出 版 物：《Chinese Journal of Electrical Engineering》 (中国电气工程学报（英文）)

年 卷 期：2020年第6卷第3期

页      面：106-114页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 080201[工学-机械制造及其自动化] 

基　　金：Supported by the National Key Research and Development Program of China(2017YFB1201003-020) the Science and Technology Project of Gansu Province(18YF1FA058) 

主　　题：Deep belief network(DBN) fault diagnosis genetic algorithm PV array recognition accuracy 

摘      要：When using deep belief networks(DBN)to establish a fault diagnosis model,the objective function easily falls into a local optimum during the learning and training process due to random initialization of the DBN network bias and weights,thereby affecting the computational *** address the problem,a fault diagnosis method based on a deep belief network optimized by genetic algorithm(GA-DBN)is *** method uses the restricted Boltzmann machine reconstruction error to structure the fitness function,and uses the genetic algorithm to optimize the network bias and weight,thus improving the network accuracy and convergence *** the experiment,the performance of the model is analyzed from the aspects of reconstruction error,classification accuracy,and time-consuming *** results are compared with those of back propagation optimized by the genetic algorithm,support vector machines,and *** shows that the proposed method improves the generalization ability of traditional DBN,and has higher recognition accuracy of photovoltaic array faults.