[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128578-en":3,"doc-seo-128578-105":30,"detail-sidebar-cat-0-en-105":92},{"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},128578,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Lightning Location and Peak Current Estimation From Lightning-Induced Voltages on Transmission Lines - With a Machine Learning Approach","A machine-learning-based regression framework estimates cloud-to-ground lightning location and peak current directly from time-domain waveforms of lightning-induced voltages measured on overhead transmission lines. The method applies principal component analysis (PCA) to extract informative features and reduce the input dimension, then trains a shallow neural network on the PCA outputs. Results indicate location accuracy comparable to or better than lightning location systems (LLS) and peak current estimates more accurate than LLS, while offering broader applicability than direct tower measurements. The approach also reduces cost by avoiding dedicated instrumentation.","Lightning Location and Peak Current Estimation From Lightning-Induced Voltages on Transmission Lines With a Machine Learning Approach  \nMartino Nicora , Member, IEEE, Mauro Tucci, Senior Member, IEEE, Sami Barmada, Senior Member, IEEE, Massimo Brignone , Member, IEEE, and Renato Procopio, Senior Member, IEEE  \nAbstract—In this article, a machine-learning-based model for the regression of cloud-to-ground lightning location and peak current from time-domain waveforms of lightning-induced voltage measurements on overhead transmission lines is presented. A principal component analysis (PCA) procedure is applied for extracting significant features and decreasing the dimension of the input vector. Then, a shallow neural network is trained with the results of the PCA. The obtained results show that the proposed approach can be the base for a tool able to regress lighting location with an accuracy comparable to or even better than traditional methods [i.e., lightning location system (LLS)] and provide a peak current estimate more accurate than LLS and more actual and widespread than direct tower measurements (which are limited to a reduced number of recorded events in some specific regions). Such a tool would also have significant advantages in terms of costs, since it would not require a dedicated instrumentation.  \nIndex Terms—Lightning-induced effects, lightning location, machine learning (ML), neural networks (NNs), transients on transmission lines.  \nI. INTRODUCTION  \nLIGHTNING strokes are natural phenomena representing a  \nsignificant source of risk for people, structures (e.g., civil buildings and wind turbines), infrastructures (e.g., power transmission and distribution, and telecommunications systems), railway, aviation, and natural environments. Knowing the location of a lightning strike and its channel-base peak current (i.e., the peak value of the return stroke current waveform measured atthe base of the lightning channel [1]) is relevant for several geophysical and electrical engineering applications.  \nManuscript received 27 September 2023; revised 9 January 2024; accepted 9 February 2024 . Date of publication 27 March 2024; date of current version 13 June 2024 . This work was supported in part by the Italian Ministry for Education, University, and Research through the project PRIN 2022 under Grant 20224CL7HM and in part by the European Union through the NextGenerationEU project. (Corresponding author: Martino Nicora.)  \nMartino Nicora, Massimo Brignone, and Renato Procopio are with Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, I-16145 Genoa, Italy (e-mail: [martino.nicora@edu.unige.it](martino.nicora@edu.unige.it); [massimo.brignone@unige.it](massimo.brignone@unige.it); re[nato.procopio@unige.it](nato.procopio@unige.it)).  \nMauro Tucci and Sami Barmada are with the Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, I-56126 Pisa, Italy ([e-mail: mauro.tucci@unipi.it](e-mail: mauro.tucci@unipi.it); [sami.barmada@unipi.it](sami.barmada@unipi.it)).  \nColor versions of one or more figures in this article are available at [https://doi.org/10.1109/TEMC.2024.3375452](https://doi.org/10.1109/TEMC.2024.3375452) .  \nDigital Object Identifier 10.1109/TEMC.2024.3375452  \nGeophysical researchers are interested in lightning as a precursor of severe weather events. Lightning forecasting algorithms [2], [3], [4] can be applied to track the spatial evolution of convective structures in real time [5], [6], with the aim of preventing catastrophic weather effects.  \nOn the other hand, electrical engineers need stroke location and peak current data in procedures aimed at quantifying the lightning risk for a structure or infrastructure and designing the appropriate lightning protection systems (e.g., the lightning performance assessment of an overhead power line [7],[8],[9],[10],[11]) .  \nAt present, cloud-to-ground lightning strokes are mainly localized","cbCaiu6vRvzhTsJV","https://ap.wps.com/l/cbCaiu6vRvzhTsJV","pdf",4449239,1,10,"English","en",105,"# Introduction\n## Lightning stroke risk and relevance\n## Existing lightning location systems (LLS)\n## Limitations of field-to-current inference and peak current statistics","[{\"question\":\"How does the proposed machine-learning approach estimate lightning location and peak current?\",\"answer\":\"It uses PCA to extract significant features from time-domain voltage waveforms, then trains a shallow neural network to regress lightning location and peak current from the PCA results.\"},{\"question\":\"What preprocessing step is used to handle the input signals?\",\"answer\":\"Principal component analysis (PCA) is applied to extract significant features and decrease the dimension of the input vector before neural-network training.\"},{\"question\":\"How do the estimated results compare with traditional lightning location systems and tower measurements?\",\"answer\":\"The approach provides location accuracy comparable to or better than LLS, and peak current estimates more accurate than LLS. It also offers wider applicability than direct tower measurements, which are limited in recorded events and regions.\"}]","Lightning Location and Peak Current Estimation From Lightning-Induced Voltages on Transmission Lines - With a Machine Learning Approach | PDF",1786001906,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"lightning-location-and-peak-current-estimation-from-lightning-induced-voltages-on-transmission-lines-with-a-machine-learning-approach","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/lightning-location-and-peak-current-estimation-from-lightning-induced-voltages-on-transmission-lines-with-a-machine-learning-approach/128578/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the proposed machine-learning approach estimate lightning location and peak current?","Question",{"text":76,"@type":77},"It uses PCA to extract significant features from time-domain voltage waveforms, then trains a shallow neural network to regress lightning location and peak current from the PCA results.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What preprocessing step is used to handle the input signals?",{"text":81,"@type":77},"Principal component analysis (PCA) is applied to extract significant features and decrease the dimension of the input vector before neural-network training.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the estimated results compare with traditional lightning location systems and tower measurements?",{"text":85,"@type":77},"The approach provides location accuracy comparable to or better than LLS, and peak current estimates more accurate than LLS. 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