Machine Learning-Based Lightning Localization Algorithm Using Lightning-Induced Voltages on Transmission Lines

In this article, we present a machine learning-based method to locate lightning flashes using calculations of lightning-induced voltages on a transmission line. The proposed approach takes advantage of the preinstalled voltage measurement systems on power transmission lines to get the data. Hence, i...

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Published in:	IEEE transactions on electromagnetic compatibility Vol. 62; no. 6; pp. 2512 - 2519
Main Authors:	Karami, Hamidreza, Mostajabi, Amirhossein, Azadifar, Mohammad, Rubinstein, Marcos, Zhuang, Chijie, Rachidi, Farhad
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Algorithms Electrical measurement Extremely low frequencies Geology Gradient boosting algorithms Lightning Lightning flashes lightning localization Lightning strikes Machine learning machine learning (ML) Machine learning algorithms Model accuracy Optics Physics Power lines Power transmission lines Sensors Signal to noise ratio transients on transmission lines Transmission line measurements Transmission lines Two dimensional models Very high frequencies Very Low Frequencies Voltage measurement Lightning Localization Transients on Transmission Lines Machine Learning Gradient Boosting Algorithms
ISSN:	0018-9375, 1558-187X
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Abstract	In this article, we present a machine learning-based method to locate lightning flashes using calculations of lightning-induced voltages on a transmission line. The proposed approach takes advantage of the preinstalled voltage measurement systems on power transmission lines to get the data. Hence, it does not require the installation of additional sensors such as extremely low frequency, very low frequency, or very high frequency. The proposed model is shown to yield reasonable accuracy in estimating two-dimensional geolocations for lightning strike points for different grid sizes up to 100 × 100 km 2 . The algorithm is shown to be robust against the distance between the voltage sensors, lightning peak current, lightning current rise time, and signal to noise ratio of the input signals.
AbstractList	In this article, we present a machine learning-based method to locate lightning flashes using calculations of lightning-induced voltages on a transmission line. The proposed approach takes advantage of the preinstalled voltage measurement systems on power transmission lines to get the data. Hence, it does not require the installation of additional sensors such as extremely low frequency, very low frequency, or very high frequency. The proposed model is shown to yield reasonable accuracy in estimating two-dimensional geolocations for lightning strike points for different grid sizes up to 100 × 100 km 2 . The algorithm is shown to be robust against the distance between the voltage sensors, lightning peak current, lightning current rise time, and signal to noise ratio of the input signals. In this study, we present a Machine Learning based method to locate lightning flashes using calculations of lightning-induced voltages on a transmission line. The proposed approach takes advantage of the preinstalled voltage measurement systems on power transmission lines to get the data. Hence, it does not require the installation of additional sensors such as ELF, VLF, or VHF. The proposed model is shown to yield reasonable accuracy in estimating 2D geolocations for lightning strike points in a grid of 10x10 km 2. The median location error obtained is less than 90 m when the sensors are 2 km away from each other. The algorithm is shown to be flexible when it comes to choosing the distance between the two voltage sensors. Furthermore, the changes in the risetime of the return stroke currents had negligible effect on the geolocation accuracies. In this article, we present a machine learning-based method to locate lightning flashes using calculations of lightning-induced voltages on a transmission line. The proposed approach takes advantage of the preinstalled voltage measurement systems on power transmission lines to get the data. Hence, it does not require the installation of additional sensors such as extremely low frequency, very low frequency, or very high frequency. The proposed model is shown to yield reasonable accuracy in estimating two-dimensional geolocations for lightning strike points for different grid sizes up to 100 × 100 km2. The algorithm is shown to be robust against the distance between the voltage sensors, lightning peak current, lightning current rise time, and signal to noise ratio of the input signals.
Author	Mostajabi, Amirhossein Rubinstein, Marcos Karami, Hamidreza Rachidi, Farhad Zhuang, Chijie Azadifar, Mohammad
Author_xml	– sequence: 1 givenname: Hamidreza orcidid: 0000-0003-0118-6559 surname: Karami fullname: Karami, Hamidreza email: hamidreza.karami@epfl.ch organization: École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland – sequence: 2 givenname: Amirhossein orcidid: 0000-0001-7644-2348 surname: Mostajabi fullname: Mostajabi, Amirhossein email: amirhossein.mostajabi@epfl.ch organization: École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland – sequence: 3 givenname: Mohammad orcidid: 0000-0002-0489-1838 surname: Azadifar fullname: Azadifar, Mohammad email: mohammad.azadifar@heig-vd.ch organization: Institute for Information and Communication Technologies, Haute Ecole d'ingénierie et de Gestion du Canton de Vaud, Yverdon-les-bains, Switzerland – sequence: 4 givenname: Marcos orcidid: 0000-0003-4806-038X surname: Rubinstein fullname: Rubinstein, Marcos email: rubinstein.m@gmail.com organization: Institute for Information and Communication Technologies, Haute Ecole d'ingénierie et de Gestion du Canton de Vaud, Yverdon-les-bains, Switzerland – sequence: 5 givenname: Chijie orcidid: 0000-0002-2579-050X surname: Zhuang fullname: Zhuang, Chijie email: chijie@tsinghua.edu.cn organization: Department of Electrical Engineering, Tsinghua University, Beijing, China – sequence: 6 givenname: Farhad orcidid: 0000-0002-2169-9549 surname: Rachidi fullname: Rachidi, Farhad email: farhad.rachidi@epfl.ch organization: École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Snippet	In this article, we present a machine learning-based method to locate lightning flashes using calculations of lightning-induced voltages on a transmission... In this study, we present a Machine Learning based method to locate lightning flashes using calculations of lightning-induced voltages on a transmission line....
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SubjectTerms	Algorithms Electrical measurement Extremely low frequencies Geology Gradient boosting algorithms Lightning Lightning flashes lightning localization Lightning strikes Machine learning machine learning (ML) Machine learning algorithms Model accuracy Optics Physics Power lines Power transmission lines Sensors Signal to noise ratio transients on transmission lines Transmission line measurements Transmission lines Two dimensional models Very high frequencies Very Low Frequencies Voltage measurement
Title	Machine Learning-Based Lightning Localization Algorithm Using Lightning-Induced Voltages on Transmission Lines
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