Machine Learning-Based Lightning Localization Algorithm Using Lightning-Induced Voltages on Transmission Lines
Hamidreza Karami, Amirhossein Mostajabi, Mohammad Azadifar, Marcos Rubinstein, Chijie Zhuang, Farhad Rachidi
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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . 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.