{"title":"基于优化神经网络的光伏系统故障检测","authors":"Partha Kayal, 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, 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}
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.
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.