山东电力技术2024，Vol.51Issue(1)：52-58,76,8.DOI:10.20097/j.cnki.issn1007-9904.2024.01.006
基于时钟循环神经网络的光伏故障诊断
Photovoltaic Fault Diagnosis Based on CW-RNN
摘要
Abstract
Most photovoltaic power stations are located in harsh environment,suffer from wind,sand,rain and snow corrosion,panels are prone to multiple types of failure.How to effectively identify and locate the fault is particularly important.Therefore,a strategy for photovoltaic fault diagnosis based on the clockwork-recurrent neural network(CW-RNN)was proposed.Firstly,a simulation model of photovoltaic array system was established,the causes of photovoltaic power generation faults were analyzed,and the output characteristics of photovoltaic array under different faults were simulated.Then,the CW-RNN method was used to establish a fault diagnosis model to identify and locate photovoltaic array faults.Finally,a photovoltaic power generation fault analysis platform was built based on the real-time database system,and the performance of the proposed fault diagnosis model was verified.The effectiveness and accuracy were verified,which has certain reference significance for the efficient and accurate fault identification and location of photovoltaic power stations.关键词
光伏阵列/故障诊断/时钟循环神经网络算法/数据库/仿真平台Key words
photovoltaic array/fault diagnosis/CW-RNN/database/simulation platform分类
动力与电气工程引用本文复制引用
林永君,张世成,杨凯,李静..基于时钟循环神经网络的光伏故障诊断[J].山东电力技术,2024,51(1):52-58,76,8.基金项目
中央高校基本科研业务费专项资金资助项目(2019MS100).Special Fund for Basic Scientific Research of Central Universities(2019MS100). （2019MS100）
