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A recursive linear model detects system problems from irradiance and temperature inputs, outputting power results through the same monitoring setup. Detected defects are then categorized via a machine-learning approach into short circuit, partial shadowing, deterioration, or open-circuit, achieving 93.09% accuracy on a 5-kW plant.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/fault-classification-and-detection-for-photovoltaic-plants-using-machine-learning-algorithms/291019/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/fault-classification-and-detection-for-photovoltaic-plants-using-machine-learning-algorithms/291019.png","ImageObject",300,407,{"name":42,"@type":43},"Evangeline","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What monitoring system capabilities are described for photovoltaic plants?","Question",{"text":63,"@type":64},"The monitoring system measures environmental and electrical factors, gathers real-time and historical data, estimates efficiency metrics, and reports performance to the grid operator.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the recursive linear model detect faults?",{"text":68,"@type":64},"It uses irradiance and temperature of the PV module as inputs and outputs power using the same monitoring system to detect problems.",{"name":70,"@type":61,"acceptedAnswer":71},"How are detected defects categorized?",{"text":72,"@type":64},"A machine-learning-based algorithm classifies each defect as short circuit, partial shadowing, deterioration, or open-circuit.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},291019,1789643188,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":124,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","Fault classification and detection for photovoltaic plants using  \nmachine learning algorithms  \nShaimaa Naser Kabour1, Raghad Adnan Almalki2, Lujain A. Alghamdi2, Wujud Alharthi2,  \nNisreen B. Alshagi3  \n1Computer and Communication Engineering, Lebanese International University, Zahle, Lebanon 2Computer Engineering, Taif University, Taif, Saudi Arabia  \n3Electrical and Computer Engineering, Libyan Academy, Tripoli, Libya  \nArticle history:  \nReceived Jun 28, 2022 Revised Jun 12, 2023 Accepted Jun 17, 2023  \nKeywords:  \nFault detection Machine learning Monitoring system Photovoltaic plants Renewable energy  \nCorresponding Author:  \nUsing photovoltaic (PV) energy has increased in recently, due to new laws that aim to reduce the global use of fossil fuels. The efficiency of a PV system relies on many types of malfunctions which may cause significant energy loss during the system’s operation, besides the ecological factors. Consequently, a monitoring system (MS) capable of measuring both the environmental and electrical factors is described in order to gather real-time and historical data and estimate the plant efficiency metrics. Additionally, a recursive linear model for detecting problems in the system is presented, where the input is the irradiance and temperature of the PV module, whereas the output is the power, using the same MS. The achieved fault detection’s accuracy for the 5-kW power plant reached 93.09 percent, based on 16 days and 143 hours of failures under various situations. After detecting a defect, a machine-learningbased algorithm categorizes each defect problem as short circuit, partial shadowing, deterioration, or open-circuit. The performance of the four most prevalent supervised machine learning (ML) approaches for this assignment (Naïve Bias, decision tree, LDA, and KNN) was evaluated according to their results.  \nThis is an open access article under the CC BY-SA license.  \nShaimaa Naser Kabour  \nComputer and Communication Engineering, Lebanese International University Zahle, Lebanon  \nEmail: [shaimaakabour@gmail.com](shaimaakabour@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nDuring the last decade, the rise of renewable energy (RE) sources has accelerated, including solar energy, geothermal energy, biomass energy, hydro energy, and others. More specifically, an impressive total new capacity of 190 GW was added globally in 2018 to major hydro-power plants, which accounts for approximately half of the total power capacity installed during that year [1] . The number of solar photovoltaic (PV) plants had the most growth among the other commonly used renewable energy sources. In 2018, PV plants made up about 39 percent of the installed capacity [1] .  \nThe most convenient source of renewable energy is the sun, since it’s large and its heat and light are distributed all over the globe, depending, of course, on the alignment between the earth and the sun. For this reason, the sunrays can be harvested and converted into electrical energy through what are called solar panels, or PV panels [2] . This energy collected and transformed by PV panels can then be used in many industrial applications, as well as residential and commercial applications, which explains the fact that solar energy is becoming widely popular [3] . However, there are numerous performance challenges that must be addressed for the purpose of enhancing the overall performance of PV. PV performance can be compromised for a variety  \nof reasons, including excessive exposure to various weather conditions, such as soiling [4], wich leads to electrical and mechanical failures such as cracked cells and short-circuits [5] . Other factors that may have an impact on PV performance include PV material, battery type, panel orientation, and panel degradation. With such a high level of exposure, it becomes necessary to implement safety measures for maintaining the performance, reducing revenue losses and downtime, ensuring quick problem detection, pr","cbCaipf3gsh2adxX","https://ap.wps.com/l/cbCaipf3gsh2adxX","pdf",534936,"English","# Article Info ABSTRACT\n## 1. INTRODUCTION","[{\"question\":\"What monitoring system capabilities are described for photovoltaic plants?\",\"answer\":\"The monitoring system measures environmental and electrical factors, gathers real-time and historical data, estimates efficiency metrics, and reports performance to the grid operator.\"},{\"question\":\"How does the recursive linear model detect faults?\",\"answer\":\"It uses irradiance and temperature of the PV module as inputs and outputs power using the same monitoring system to detect problems.\"},{\"question\":\"How are detected defects categorized?\",\"answer\":\"A machine-learning-based algorithm classifies each defect as short circuit, partial shadowing, deterioration, or open-circuit.\"}]","Fault classification and detection for photovoltaic plants using machine learning algorithms | PDF",25]