{"title":"使用 ANN 和 GEO 技术检测独立光伏系统中的故障原因及其检测方法","authors":"","doi":"10.1016/j.isatra.2024.06.030","DOIUrl":null,"url":null,"abstract":"<div><p><span>Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an </span>Artificial Neural Network<span><span><span> and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system<span> to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the </span></span>Artificial Neural Network<span> (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like </span></span>genetic algorithm<span> (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.</span></span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fault causes and its detection in standalone PV system using ANN and GEO technique\",\"authors\":\"\",\"doi\":\"10.1016/j.isatra.2024.06.030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an </span>Artificial Neural Network<span><span><span> and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system<span> to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the </span></span>Artificial Neural Network<span> (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like </span></span>genetic algorithm<span> (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.</span></span></p></div>\",\"PeriodicalId\":14660,\"journal\":{\"name\":\"ISA transactions\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISA transactions\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S001905782400315X\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S001905782400315X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
采用光伏(PV)技术的发电系统因其生产效率高而越来越受欢迎。在生产过程中,部分遮光缺陷是该系统最常见的缺陷,会降低发电量和发电质量。本文提出了一种基于人工神经网络和金鹰优化技术的独立光伏系统故障预测和检测方法,以恢复光伏系统的最佳性能并对其进行诊断。所提出的技术结合了人工神经网络(ANN)和金鹰优化(GEO)算法。这项工作的主要贡献在于提高光伏系统的性能。其结果是使用了人工神经网络进行分类和识别。GEO 的使用为 ANN 的训练提供了有效的优化技术,缩短了训练时间,提高了模型的准确性。提议的技术在 MATLAB 网站上执行,并与遗传算法(GA)、大象放牧优化(EHO)和粒子群优化(PSO)等不同的现有技术进行对比。研究结果表明,在检测和诊断光伏系统缺陷方面,所提出的技术比现有方法更准确、更有效。
Fault causes and its detection in standalone PV system using ANN and GEO technique
Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.
期刊介绍:
ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.