Machine learning-based lightning localization algorithm using lightning-induced voltages on transmission lines

Karami, Hamidreza (EPFL, Lausanne, Switzerland ; Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran) ; Mostajabi, Amirhossein (EPFL, Lausanne, Switzerland) ; Azadifar, Mohammad (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Rubinstein, Marcos (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Zhuang, Chijie (Department of Electrical Engineering, Tsinghua University, Beijing) ; Rachidi, Farhad (EPFL, Lausanne, Switzerland)

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.


Note: early access


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Date:
2020-03
Pagination:
8 p.
Published in:
IEEE Transactions on Electromagnetic Compatibility
DOI:
ISSN:
0018-9375
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-05-19, last modified 2020-05-22

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