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Journal Articles IEEE Transactions on Electromagnetic Compatibility Year : 2020

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

Abstract

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.
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Dates and versions

hal-03589923 , version 1 (26-02-2022)

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Hamidreza Karami, Amirhossein Mostajabi, Mohammad Azadifar, Marcos Rubinstein, Chijie Zhuang, et al.. Machine Learning-Based Lightning Localization Algorithm Using Lightning-Induced Voltages on Transmission Lines. IEEE Transactions on Electromagnetic Compatibility, 2020, 62 (6), pp.2512-2519. ⟨10.1109/TEMC.2020.2978429⟩. ⟨hal-03589923⟩
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