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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:0018-9375
1558-187X
DOI:10.1109/TEMC.2020.2978429