[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120609-en":3,"doc-seo-120609-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120609,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Fault Detection in Photovoltaic Systems Using a Machine Learning Approach - read online free","Intelligent fault monitoring for photovoltaic systems supports reliable energy generation while lowering maintenance expenses. This study investigates machine-learning methods for autonomous detection and classification of faults caused by partial shading and dirt accumulation in PV modules. Multiple algorithms—including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression—are trained and compared using data from two real photovoltaic systems with different module characteristics and power ratings. Measurements include voltage, current, ambient temperature, and irradiance under normal and fault-simulated conditions. Results show high accuracy on the training system, but performance drops when applied to a different system, indicating the need to include each new PV system in training. The Artificial Neural Network achieved precision above 98%.","Received 19 February 2025, accepted 28 February 2025, date of publication 4 March 2025, date of current version 12 March 2025. Digital Object Identifier 10.1109/ACCESS.2025.3547838  \nFault Detection in Photovoltaic Systems Using a Machine Learning Approach  \nJOSSIAS ZWIRTES1, FAUSTO BASTOS LÍBANO2, LUÍS ALVARO DE LIMA SILVA3, AND EDISON PIGNATON DE FREITAS1,4,(Senior Member, IEEE)  \n1Graduate Program in Electrical Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil  \n2Electrical Engineering Department, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil  \n3Applied Computing Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil  \n4 School of Information Technology, Halmstad University, 301 18 Halmstad, Sweden Corresponding author: Jossias Zwirtes ([jossias.zwirtes@ufrgs.br](jossias.zwirtes@ufrgs.br))  \nThis study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) -Finance Code 001 . The CAPES – Coordenação de Aperfeiçoamento de Pessoal de Nível Superior has an agreement with IEEE that enables researchers to publish articles without open access fees (APCs) .  \nABSTRACT Research and development of intelligent fault monitoring in photovoltaic systems are crucial for efficient energy generation. In response to the industry’s demand for innovative solutions to enhance energy output and reduce maintenance costs, this study explores machine-learning approaches for the autonomous detection and classification of faults caused by partial shading and dirt accumulation in photovoltaic modules. The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. The research explored data collected from two real photovoltaic systems, each with distinct module characteristics and power ratings. Data were gathered for systems without faults, with faults simulated by partial shading, and faults simulated by dirt accumulation. Crucial information, including voltage, current, ambient temperature, and irradiance, was recorded to assess and classify these kinds of faults. This study presents three main contributions: the implementation and comparison of multiple machine learning models for fault detection, an investigation into the feasibility of identifying these faults using only electrical and environmental data, and an analysis of model performance in a photovoltaic system different from the one used for training. The results indicate that models trained on a specific system achieve high accuracy but face challenges when applied to systems with different characteristics, suggesting that each new photovoltaic system to be monitored should be included in the training phase to enhance classification performance. Noteworthy results were obtained with the Artificial Neural Network model, achieving precision values exceeding 98% .  \nINDEX TERMS Photovoltaic faults, machine learning, partial shading, dirt accumulation.  \nI. INTRODUCTION  \nChallenges in the efficient monitoring of photovoltaic system faults can be directly linked to significant long-term losses in the photovoltaic industry. Although early detection of faults causing intermittent photovoltaic module generation can enhance overall production, novel intelligent technologies are still required to improve the process of monitoring photovoltaic plants. It is hypothesized that an enhanced fault detection system can contribute to increased electricity  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Ahmed F. Zobaa .  \nproduction, improved plant maintenance efficiency, and reduced costs associated with photovoltaic fault detection systems.  \nNumerous faults can occur during the operation of photovoltaic systems, which may stem from electrical and non-electrical issues. The works in [1] and [","cbCaidgXoxQtRGjO","https://ap.wps.com/l/cbCaidgXoxQtRGjO","pdf",1906057,1,16,"English","en",105,"# Introduction\n## Fault monitoring challenges in photovoltaic systems\n## Prior methods for fault detection and classification\n## Techniques for partial shading diagnosis\n## Gaps in dirt-accumulation detection\n## Study contributions and approach","[{\"question\":\"Which faults are targeted in this study for photovoltaic systems?\",\"answer\":\"The study targets faults caused by partial shading and dirt accumulation in photovoltaic modules, simulated and recorded using electrical and environmental measurements.\"},{\"question\":\"Which machine-learning algorithms are evaluated for fault detection?\",\"answer\":\"The evaluated models include Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression.\"},{\"question\":\"What inputs are used to detect and classify faults?\",\"answer\":\"The research records voltage, current, ambient temperature, and irradiance to assess and classify the fault types.\"}]","Fault Detection in Photovoltaic Systems Using a Machine Learning Approach - 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