基于深度信念网络的光伏阵列故障诊断

doi:10.11930/j.issn.1004-9649.201901066

中国电力 ›› 2019, Vol. 52 ›› Issue (12): 105-112.DOI: 10.11930/j.issn.1004-9649.201901066

基于深度信念网络的光伏阵列故障诊断

陶彩霞, 王旭, 高锋阳

兰州交通大学电气工程及其自动化学院, 甘肃兰州 730070

收稿日期:2019-01-20 修回日期:2019-08-05 发布日期:2019-12-05
通讯作者: 王旭(1992-),男,通信作者,硕士研究生,从事光伏系统故障诊断研究,E-mail:1401614851@qq.com
作者简介:陶彩霞(1972-),女,教授,从事电机及控制、光伏系统健康管理研究,E-mail:1733425004@qq.com;高锋阳(1970-),男,教授,从事大功率电源、配电网健康管理等研究,E-mail:329365048@qq.com
基金资助:
国家重点研发计划资助项目（2017YFB1201003-020）；甘肃省科技项目（18YF1FA058）

Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network

TAO Caixia, WANG Xu, GAO Fengyang

Department of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Received:2019-01-20 Revised:2019-08-05 Published:2019-12-05
Supported by:
This work is supported by the National Key Research and Development Program of China (No.2017YFB1201003-020), Science and Technology Project of Gansu Province of China (No.18YF1FA058)

摘要/Abstract

摘要： 光伏阵列所处环境恶劣严峻，导致故障频发。为提高光伏阵列故障诊断精度，针对光伏阵列的常见故障类型，提出基于深度信念网络（deep belief networks，DBN）的故障诊断方法。利用Matlab仿真模拟获取实验特征参数，建立以光伏阵列5种运行状态为输出的故障诊断模型；根据深度信念网络的特点，通过识别实验，分析不同训练集、训练周期以及受限玻尔兹曼机（restricted boltzmann machine，RBM）层数等对模型性能的影响，并从整体诊断精度和各类型故障诊断精度2方面，与模糊C均值聚类（fuzzy C-means clustering，FCM）、支持向量机（support vector machine，SVM）和BP神经网络（back propagation neural networks，BPNN）方法进行对比。实验结果表明，该方法适用于光伏阵列故障分类，相比于其他诊断模型，有效提高了故障识别准确率。

关键词: 光伏阵列, 故障诊断, 特征参数, 深度信念网络, 识别准确率

Abstract: The environment of the PV array is harsh and severe, resulting in frequent faults. In order to improve the accuracy of PV array fault diagnosis, a deep belief networks (DBN) based fault diagnosis method is proposed for the common fault types of PV arrays. The experimental feature parameters was obtained by Matlab simulation and the fault diagnosis model with the five operating states of the PV array is established. According to the characteristics of the DBN, the impacts of training sets, training periods and restricted boltzmann machine (RBM) layers on the model performance are analyzed through recognition experiments. Compared with the fuzzy C-means clustering (FCM), the support vector machine (SVM) and the back-propagation neural network (BPNN) method from the overall diagnostic accuracy and different types of fault diagnostic accuracy. The results show that the method is suitable for fault classification of photovoltaic arrays, and it improves the accuracy of fault identification effectively compared with other diagnostic models.

Key words: PV array, fault diagnosis, feature parameters, deep belief network, recognition accuracy

中图分类号:

TM614

陶彩霞, 王旭, 高锋阳. 基于深度信念网络的光伏阵列故障诊断[J]. 中国电力, 2019, 52(12): 105-112.

TAO Caixia, WANG Xu, GAO Fengyang. Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network[J]. Electric Power, 2019, 52(12): 105-112.

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基于深度信念网络的光伏阵列故障诊断

Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network

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基于深度信念网络的光伏阵列故障诊断

Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network

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