[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121664-en":3,"doc-seo-121664-105":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},121664,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","DISTRIBUTION NETWORK FAULT PREDICTION UTILISING PROTECTION RELAY DISTURBANCE RECORDINGS AND MACHINE LEARNING","As electricity use expands, grid operators face higher reliability demands to reduce interruptions and improve fault localization, isolation, and supply restoration. This paper presents a machine learning–based fault prediction approach aimed at identifying incipient faults early. By predicting faults up to about a week before occurrence, transmission and distribution system operators can take preventive actions to avoid customer outages. The method relies on existing current and voltage measurements for timely warnings without adding new sensors.","27th International Conference on Electricity Distribution Rome, 12-15 June 2023  Paper n° 10856   \nDISTRIBUTION NETWORK FAULT PREDICTION UTILISING PROTECTION RELAY DISTURBANCE RECORDINGS AND MACHINE LEARNING  \nEbrahim BALOUJIEneryield – Sweden  \n[balouji@eneryield.com](balouji@eneryield.com)  \nPetri Hovila ABB – Finland  \n[petri.hovila@fi.abb.com](petri.hovila@fi.abb.com)  \nKarl BÄCKSTRÖMEneryield – Sweden  \n[kalle@eneryield.com](kalle@eneryield.com)  \nHenry NIVERIABB – Finland  \n[henry.x.niveri@fi.abb.com](henry.x.niveri@fi.abb.com)  \nViktor OLSSON Eneryield – Sweden  \n[viktor@eneryield.com](viktor@eneryield.com)  \nAnna KULMALA ABB – Finland  \n[anna.kulmala@fi.abb.com](anna.kulmala@fi.abb.com)  \nAri SALOVaasan Sähköverkko – Finland  \n[ari.salo@vaasansahkoverkko.fi](ari.salo@vaasansahkoverkko.fi)  \nABSTRACT  \nAs society becomes increasingly reliant on electricity, the reliability requirements for electricity supply continue to rise. In response, transmission/distribution system operators (T/DSOs) must improve their networks and operational practices to reduce the number of interruptions and enhance their fault localization, isolation, and supply restoration processes to minimize fault duration. This paper proposes a machine learningbased fault prediction method that aims to predict incipient faults, allowing T/DSOs to take action before the fault occurs and prevent customer outages.  \nINTRODUCTION  \nThe increasing scope and complexity of electrical power systems across all sectors, including generation, transmission, distribution, and load systems, is resulting ina higher frequency of faults. The most common types of grid faults are partial or complete short circuits of power lines to the ground or among themselves. These faults can lead to significant financial losses and decrease the reliability of the electrical system. Utilities and large industrial plants, which often have extensive power line systems, are prone to faults due to various reasons, including:  \n• Aging and wear and tear of power lines during operation  \n• Using power lines that are not suitable for the intended application  \n• Mechanical failure, such as damage to power lines during installation or subsequent use  \n• Degradation of power line sheaths and insulation due to e.g., extreme temperatures, chemicals, weather, or abrasion  \n• Moisture build-up in insulation  \n• Electrical overloading  \n• Birds or other animals  \n• Vegetation that is too close or trees falling on  \npower lines  \nEarly fault prediction can significantly benefit grid operators by enabling them to address potential issues before they lead to failures. This can improve the overall reliability of the grid, resulting in decreased operational costs and reduced revenue loss, as well as ensuring the continuity of power delivery to end users. Before a fault happens, the grid often suffers from precursor symptoms, making it possible to predict faults using appropriate models that detect these symptoms. There are some existing studies on the prediction and analysis of grid faults, which we will briefly discuss here. One of the most well-known approaches to predict power line failures, that has been studied over the past few years, is by analysing partial discharges [1-5] . However, this analysis requires expensive measurement tools and is very challenging in noisy situations, which is often the case in large-scale grids. Another drawback with this approach is that it is not feasible in the case of fault prediction in underground and underwater cables. Additionally, not all faults originate from insulation degradation, but due to other reasons, like the ones mentioned above, which the partial dischargebased methods are not suitable for.  \nOther methods for predicting faults have also been investigated in recent years, such as using temperature sensors on power lines [6-9] and monitoring power lines with unmanned aerial vehicles [10-12] . However, these methods have limitations in detecting faults","cbCaigfnhOJzt1fN","https://ap.wps.com/l/cbCaigfnhOJzt1fN","pdf",585104,1,5,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Data\n## Forecasting pipeline","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper targets the need to improve grid reliability by reducing interruptions and enhancing fault localization, isolation, and restoration through early fault prediction.\"},{\"question\":\"How does the proposed method predict faults?\",\"answer\":\"It uses a purely data-driven pipeline: extracting relevant features from anomalous current and voltage measurements, filtering irrelevant data, and applying an LSTM-based deep neural network to generate warnings for imminent faults.\"},{\"question\":\"What data sources are required, and are additional sensors needed?\",\"answer\":\"The approach uses existing current and voltage measurements and I/O status data shared via IEC 61850-9, avoiding the installation of additional sensors.\"}]","DISTRIBUTION NETWORK FAULT PREDICTION UTILISING PROTECTION RELAY DISTURBANCE RECORDINGS AND MACHINE LEARNING | 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problem does the paper address?","Question",{"text":75,"@type":76},"The paper targets the need to improve grid reliability by reducing interruptions and enhancing fault localization, isolation, and restoration through early fault prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method predict faults?",{"text":80,"@type":76},"It uses a purely data-driven pipeline: extracting relevant features from anomalous current and voltage measurements, filtering irrelevant data, and applying an LSTM-based deep neural network to generate warnings for imminent faults.",{"name":82,"@type":73,"acceptedAnswer":83},"What data sources are required, and are additional sensors needed?",{"text":84,"@type":76},"The approach uses existing current and voltage measurements and I/O status data shared via IEC 61850-9, avoiding the installation of additional 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