基于优化神经网络的光伏系统故障检测

IF 1.204 Q3 Energy
Partha Kayal,  Abdul Vasih T. V.
{"title":"基于优化神经网络的光伏系统故障检测","authors":"Partha Kayal,&nbsp; Abdul Vasih T. V.","doi":"10.3103/S0003701X22600850","DOIUrl":null,"url":null,"abstract":"<p>Fault detection in photovoltaic (PV) arrays is one of the prime challenges for the operation of solar power plants. This paper proposes an artificial neural network (ANN) based fault detection approach. Partial shading, line-to-line fault, open circuit fault, short circuit fault, and ground fault in a PV array have been investigated, and a data set is synthesized to evaluate the impact on maximum power amplitude and number of power peaks under various exposure of irradiance and temperature. The ANN model has been trained considering irradiance, temperature, maximum power, and the number of power peaks corresponding to the different faulty conditions and non-fault situations. The considered ANN model has been optimized in order to increase the accuracy of fault identification. A particle swarm optimization-based algorithm has been employed to find the optimum number of neurons in the hidden layers to achieve the highest possible prediction accuracy on the test data set. The performance of the optimized neural network has been further cross-validated by an arranged data set containing all the types of faulty conditions. The effectiveness of the proposed technique is verified by comparing the results with existing methods.</p>","PeriodicalId":475,"journal":{"name":"Applied Solar Energy","volume":"59 3","pages":"269 - 282"},"PeriodicalIF":1.2040,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fault Detection in Photovoltaic Systems Using Optimized Neural Network\",\"authors\":\"Partha Kayal,&nbsp; Abdul Vasih T. V.\",\"doi\":\"10.3103/S0003701X22600850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Fault detection in photovoltaic (PV) arrays is one of the prime challenges for the operation of solar power plants. This paper proposes an artificial neural network (ANN) based fault detection approach. Partial shading, line-to-line fault, open circuit fault, short circuit fault, and ground fault in a PV array have been investigated, and a data set is synthesized to evaluate the impact on maximum power amplitude and number of power peaks under various exposure of irradiance and temperature. The ANN model has been trained considering irradiance, temperature, maximum power, and the number of power peaks corresponding to the different faulty conditions and non-fault situations. The considered ANN model has been optimized in order to increase the accuracy of fault identification. A particle swarm optimization-based algorithm has been employed to find the optimum number of neurons in the hidden layers to achieve the highest possible prediction accuracy on the test data set. The performance of the optimized neural network has been further cross-validated by an arranged data set containing all the types of faulty conditions. The effectiveness of the proposed technique is verified by comparing the results with existing methods.</p>\",\"PeriodicalId\":475,\"journal\":{\"name\":\"Applied Solar Energy\",\"volume\":\"59 3\",\"pages\":\"269 - 282\"},\"PeriodicalIF\":1.2040,\"publicationDate\":\"2023-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Solar Energy\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://link.springer.com/article/10.3103/S0003701X22600850\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Energy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Solar Energy","FirstCategoryId":"1","ListUrlMain":"https://link.springer.com/article/10.3103/S0003701X22600850","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Energy","Score":null,"Total":0}
引用次数: 0

摘要

光伏阵列的故障检测是太阳能发电厂运行的主要挑战之一。本文提出了一种基于人工神经网络的故障检测方法。研究了光伏阵列中的部分遮光、线对线故障、开路故障、短路故障和接地故障,并合成了一个数据集,以评估在不同辐照和温度下对最大功率幅度和功率峰值数量的影响。神经网络模型的训练考虑了辐照度、温度、最大功率以及与不同故障条件和非故障情况相对应的功率峰值数量。为了提高故障识别的准确性,对所考虑的神经网络模型进行了优化。采用了一种基于粒子群优化的算法来寻找隐藏层中神经元的最佳数量,以在测试数据集上实现尽可能高的预测精度。通过包含所有类型故障条件的排列数据集,优化神经网络的性能得到了进一步的交叉验证。通过与现有方法的比较,验证了该技术的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Fault Detection in Photovoltaic Systems Using Optimized Neural Network

Fault Detection in Photovoltaic Systems Using Optimized Neural Network

Fault detection in photovoltaic (PV) arrays is one of the prime challenges for the operation of solar power plants. This paper proposes an artificial neural network (ANN) based fault detection approach. Partial shading, line-to-line fault, open circuit fault, short circuit fault, and ground fault in a PV array have been investigated, and a data set is synthesized to evaluate the impact on maximum power amplitude and number of power peaks under various exposure of irradiance and temperature. The ANN model has been trained considering irradiance, temperature, maximum power, and the number of power peaks corresponding to the different faulty conditions and non-fault situations. The considered ANN model has been optimized in order to increase the accuracy of fault identification. A particle swarm optimization-based algorithm has been employed to find the optimum number of neurons in the hidden layers to achieve the highest possible prediction accuracy on the test data set. The performance of the optimized neural network has been further cross-validated by an arranged data set containing all the types of faulty conditions. The effectiveness of the proposed technique is verified by comparing the results with existing methods.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Applied Solar Energy
Applied Solar Energy Energy-Renewable Energy, Sustainability and the Environment
CiteScore
2.50
自引率
0.00%
发文量
0
期刊介绍: Applied Solar Energy  is an international peer reviewed journal covers various topics of research and development studies on solar energy conversion and use: photovoltaics, thermophotovoltaics, water heaters, passive solar heating systems, drying of agricultural production, water desalination, solar radiation condensers, operation of Big Solar Oven, combined use of solar energy and traditional energy sources, new semiconductors for solar cells and thermophotovoltaic system photocells, engines for autonomous solar stations.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信