[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120247-en":3,"doc-seo-120247-105":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":4,"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},120247,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Application of Machine Learning in Fault Detection And Classification in Power Transmission Lines - Research and Results","Electrical faults are a major cause of damage to electrical equipment, leading to bushfires, outages, and power shortages. Detecting and classifying faults supports reliable power delivery, environmental protection, equipment preservation, and improved service quality. The study analyzes Matlab-modeled data from a 11 kV network with four generators located in pairs at both ends of a transmission line, applying machine learning for fault detection and supervised classifiers for fault categorization. Decision trees achieve the highest detection rate with a confusion-matrix-based comparison.","Application of Machine Learning in Fault Detection And Classification in Power Transmission Lines  \nM.E. Tshodi T. *1, A. Ntumba N. 2, P. Mbuyi B. 3, F. Keredjim D. 4, J.J. Katshitshi M. 5, N. Kasoro M. 6, L. Kitoko S.7  \n1,2,4,6 Faculty of Science and Technology, University of Kinshasa, Kinshasa, D.R. Congo  \n2,7 Department of Electrical Engineering, Polytechnic Faculty, University of Kinshasa, Kinshasa, D.R. Congo  \n5 Department of Computer Management and Business English, Faculty of Economics, University of Kinshasa, Kinshasa, D.R. Congo  \n[email:](email:1 michel.tshodi@unikin.ac.cd)[1](email:1 michel.tshodi@unikin.ac.cd)[ ](email:1 michel.tshodi@unikin.ac.cd)[michel.tshodi@unikin.ac.cd](email:1 michel.tshodi@unikin.ac.cd), [2](2 nathanael.kasoro@unikin.ac.cd)[ nathanael.kasoro@unikin.ac.cd](2 nathanael.kasoro@unikin.ac.cd), [3](3 freddykeredjim@gmail.com)[ freddykeredjim@gmail.com](3 freddykeredjim@gmail.com), [4](4 albertntumba1994@gmail.com)[ albertntumba1994@gmail.com](4 albertntumba1994@gmail.com), [5](5 jeanjacques.katshitshi@unikin.ac.cd)[ jeanjacques.katshitshi@unikin.ac.cd](5 jeanjacques.katshitshi@unikin.ac.cd), [6](6 paul.mbuyi@unikin.ac.cd)[ paul.mbuyi@unikin.ac.cd](6 paul.mbuyi@unikin.ac.cd), [7](7 profkitoko@yahoo.fr)[ profkitoko@yahoo.fr](7 profkitoko@yahoo.fr)  \n\n| A R T I C L E I N F O |\n| --- |\n| Article history:\u003Cbr>Received 12 August 2024 Revised 06 November 2024 Accepted 09 December 2024 Available online 30 December 2024 |\n| Keywords:\u003Cbr>Fault detection;\u003Cbr>Analytical models;\u003Cbr>Machine learning algorithms; Power transmission lines; electrical faults detection; |\n\nIEEE style in citing this article:  \nM. E. Tshodi et al.,“Application Of Machine Learning in Fault Detection And Classification In Power Transmission Lines ,” Journal of Innovation Information Technology and Application (JINITA), vol. 6, no. 2, pp. 118–129, Dec. 2024.  \nA B S T R A C T  \nElectrical faults have been identified as a significant contributing factor to electrical equipment damage. Such incidents can potentially result in a range of adverse consequences, including bushfires, electrical outages, and power shortages. The detection and classification of faults facilitates the delivery of superior quality of service, the preservation of the environment, the prevention of equipment damage, and the satisfaction of electricity line subscribers. In this study, we analyze the data from an electrical network comprising four generators of 11 kV, which have been modeled in Matlab. The generators are situated in pairs at either end of the transmission line. Subsequently, machine learning techniques are employed to detect faultsin the transmission between lines, and machine learning models are utilized to classify the faults. Four distinct supervised machine learning classifiers are employed for comparison purposes, with the results presented in a confusion matrix. The results demonstrated that decision trees are particularly well-suited to this task, with an 88.6205% detection rate and a slightly higher accuracy than the random forest algorithm (87.9212% detection rate) . The K-nearest neighbor's approach yielded a lower result (80.4196% of faults detected), while logistic regression demonstrated the lowest performance, with 34.5836% of faults detected. Six fault categories were found in the dataset: No-Fault (2365 occurrences), Line A Line B to Ground Fault (1134 occurrences), ThreePhase with Ground (1133 occurrences), Line-to-Line AB (1129 occurrences), Three-Phase (1096 occurrences) and finally Line-to-Line  \n with Ground BC (1004 occurrences) .   \n1. INTRODUCTION  \nThe significance of electrical energy is self-evident in the context of the expansion of various industrial sectors, including chemical, mining, health, and others. All these industries require electrical energy, and the demand for it continues to grow daily. Generation, transmission, and distribution systems represent the primary components of an electric power system. Generating","cbCaifh4UrfXR47J","https://ap.wps.com/l/cbCaifh4UrfXR47J","pdf",1267571,1,12,"English","en",105,"# Introduction\n## Fault detection methods and limitations\n## Proposed machine learning approach\n# Fault dataset and fault categories\n# Machine learning classifiers and comparison\n## Confusion matrix results\n## Decision trees vs other models","[{\"question\":\"Why is fault detection and classification important for power transmission lines?\",\"answer\":\"Fault detection and classification help ensure better service quality, reduce equipment damage, prevent environmental risks, and support reliable electricity supply by identifying faults promptly.\"},{\"question\":\"How was the study data generated and modeled?\",\"answer\":\"The work uses Matlab models of an electrical network with four 11 kV generators, arranged in pairs at each end of the transmission line, and then uses the resulting data for detection and classification.\"},{\"question\":\"Which supervised machine learning model performed best and how?\",\"answer\":\"Decision trees performed best, reaching an 88.6205% fault detection rate, slightly higher than the random forest model (87.9212%), based on confusion-matrix results.\"}]","Application of Machine Learning in Fault Detection And Classification in Power Transmission Lines - 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