Prediction of lightning flashover on overhead distribution lines from preliminary breakdown voltages based on deep learning

Lightning strikes represent a major source of risk for power systems, particularly for overhead distribution lines, where indirect events can induce dangerous overvoltages through electromagnetic coupling. This work represents a first step towards the development of a predictive lightning protection system based on the detection of Preliminary Breakdown (PB) pulses, i.e., sequences of lower-intensity atmospheric discharges typically preceding the Return Stroke (RS) by several tens of milliseconds. Synthetic PB- and RS-induced voltage waveforms are generated using a dedicated simulation tool. Time-domain samples of PB-induced voltages from two sensors are used to train a Deep Learning (DL) model to predict the maximum RS-induced voltage and assess whether it may lead to a flashover event. The line Critical FlashOver Voltage (CFO), increased by 50%, is used as a threshold for classification between safe and dangerous conditions. The proposed model achieves promising performance, paving the way for further development of a preventive lightning protection system (to be combined with state-of-the-art methods) exploiting PB-induced voltage measurements.