{"title":"通过基于阻抗的结构健康监测,利用神经网络精确定位光伏太阳能的损坏位置","authors":"Billel Sakhria, Brahim Hamaidi, Mahamed Djemana, Naamane Benhassine","doi":"10.1007/s00202-024-02700-5","DOIUrl":null,"url":null,"abstract":"<p>Accurate fault detection and monitoring are crucial for maintaining photovoltaic (PV) system performance. While previous studies mainly focused on PV system faults, they often lack a comprehensive approach to integrating advanced diagnostic techniques, leading to duplicated research efforts and insufficient exploration of novel methodologies. This paper investigates the use of the finite element method to simulate the electromechanical impedance technique for fault detection and classification in PV systems. A 3D finite element model of a photovoltaic panel was created using ANSYS software to understand the basics of this technique. Studies on different locations of structural cracks were conducted to assess their impact on PV system output. For model verification, various fault and normal state simulation datasets were collected, normalized using data from piezoelectric sensors, and preprocessed. These datasets were then fed into an extreme learning machine (ELM) algorithm designed to predict and classify damage locations. The results highlight the superior efficacy of the ELM algorithm in defect detection, boasting an impressive overall accuracy rate of 85%.</p>","PeriodicalId":50546,"journal":{"name":"Electrical Engineering","volume":"72 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Harnessing neural networks for precise damage localization in photovoltaic solar via impedance-based structural health monitoring\",\"authors\":\"Billel Sakhria, Brahim Hamaidi, Mahamed Djemana, Naamane Benhassine\",\"doi\":\"10.1007/s00202-024-02700-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Accurate fault detection and monitoring are crucial for maintaining photovoltaic (PV) system performance. While previous studies mainly focused on PV system faults, they often lack a comprehensive approach to integrating advanced diagnostic techniques, leading to duplicated research efforts and insufficient exploration of novel methodologies. This paper investigates the use of the finite element method to simulate the electromechanical impedance technique for fault detection and classification in PV systems. A 3D finite element model of a photovoltaic panel was created using ANSYS software to understand the basics of this technique. Studies on different locations of structural cracks were conducted to assess their impact on PV system output. For model verification, various fault and normal state simulation datasets were collected, normalized using data from piezoelectric sensors, and preprocessed. These datasets were then fed into an extreme learning machine (ELM) algorithm designed to predict and classify damage locations. The results highlight the superior efficacy of the ELM algorithm in defect detection, boasting an impressive overall accuracy rate of 85%.</p>\",\"PeriodicalId\":50546,\"journal\":{\"name\":\"Electrical Engineering\",\"volume\":\"72 1\",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electrical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00202-024-02700-5\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electrical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00202-024-02700-5","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
准确的故障检测和监控对于保持光伏(PV)系统性能至关重要。以往的研究主要关注光伏系统故障,但往往缺乏整合先进诊断技术的综合方法,导致研究工作重复,对新方法的探索不足。本文研究了利用有限元法模拟机电阻抗技术对光伏系统进行故障检测和分类。为了解该技术的基本原理,我们使用 ANSYS 软件创建了光伏面板的三维有限元模型。对结构裂缝的不同位置进行了研究,以评估其对光伏系统输出的影响。为验证模型,收集了各种故障和正常状态模拟数据集,并使用压电传感器的数据进行归一化和预处理。然后将这些数据集输入极端学习机 (ELM) 算法,该算法旨在预测和分类损坏位置。结果凸显了 ELM 算法在缺陷检测方面的卓越功效,总体准确率高达 85%,令人印象深刻。
Harnessing neural networks for precise damage localization in photovoltaic solar via impedance-based structural health monitoring
Accurate fault detection and monitoring are crucial for maintaining photovoltaic (PV) system performance. While previous studies mainly focused on PV system faults, they often lack a comprehensive approach to integrating advanced diagnostic techniques, leading to duplicated research efforts and insufficient exploration of novel methodologies. This paper investigates the use of the finite element method to simulate the electromechanical impedance technique for fault detection and classification in PV systems. A 3D finite element model of a photovoltaic panel was created using ANSYS software to understand the basics of this technique. Studies on different locations of structural cracks were conducted to assess their impact on PV system output. For model verification, various fault and normal state simulation datasets were collected, normalized using data from piezoelectric sensors, and preprocessed. These datasets were then fed into an extreme learning machine (ELM) algorithm designed to predict and classify damage locations. The results highlight the superior efficacy of the ELM algorithm in defect detection, boasting an impressive overall accuracy rate of 85%.
期刊介绍:
The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed.
Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).