Martino Nicora

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 proposes 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 weaker atmospherics discharges typically preceding the Return Stroke (RS) by some tens of milliseconds. Synthetic PB and RS induced voltage waveforms are generated using a dedicated simulation tool. Time domains 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, claiming for further development of a lightning preventive protection (to be combined with state-of-art methods) exploiting the PB pulses induced voltage measurements.

Preliminary Breakdown Pulses (PBPs) are electromagnetic signatures of the process occurring in the early stages of a Cloud-to-Ground (CG) lightning flash, leading to the initiation of the downward stepped leader followed by the first Return Stroke (RS) retracing the leader channel. Their analysis can offer insights into the initiation of the lightning discharge process. Among the various lightning processes, PBPs remain less explored, although their study has the potential to improve lightning prediction and protection strategies. This study focuses on modeling PBPs using an engineering-model like approach (i.e, the Modified Transmission Line model by Karunarathne and coworkers, MTLK), exploiting its similarities with typical models for RS processes. The primary objective is to optimize the MTLK model parameters by fitting experimental electric field data from more than 3000 negative CG lightning flashes with peak current higher than 50 kA (as reported by the U.S. National Lightning Detection Network, NLDN). The fitting process, treated as an optimization problem, employs the Multivariate Ant Colony Algorithm for Continuous Optimization toolbox to minimize errors between predicted and observed electric fields. In this contribution, the statistical distributions of the parameters of the MTLK model are obtained, allowing the simulation of random lightning events in terms of PBPs and the corresponding RS. This could be useful for identifying the relationship between RS and PBP effects (e.g., on the induced voltage on power systems) and developing preventive tools for lightning protection based on PBP detection.

This study investigates the evolution of lightning activity in Italy by comparing two biennia: 2010–2011 and 2020–2021. Lightning data were obtained from a lightning location system (the SIRF network) and processed to focus exclusively on cloud-to-ground (CG) strokes. The analysis covers both land and surrounding sea areas, with data filtered and validated according to the study objectives. A detailed statistical comparison of CG activity between the two periods is presented, along with an assessment of spatial distributions across the Italian territory. Furthermore, correlations between CG stroke occurrence and both sea surface temperature and land surface temperature are examined. The findings highlight temporal and spatial variations in lightning patterns over the decade and provide insights into their possible relationship with environmental conditions.

The electromagnetic fields (EMFs) produced by a lightning discharge are usually computed by supposing a vertical channel. However, the tortuous path of channels represents a realistic feature of lightning strikes and can drastically modify the radiated EMFs. Numerical methods for their computation have been developed, mainly based on the superposition law as applied to consecutive rectilinear segments, properly oriented in order to create a tortuous path; however, such methods require a high computational effort, which may be unfeasible for realistic problems. As an alternative approach, this work introduces an equivalent tortuous function, which aims at taking into account the channel tortuosity in the EMF expressions produced by a vertical channel. The proposed function is obtained as the regularized solution of an appropriate inverse problem, and is validated against several tortuous lightning channels.

In this article, a machine-learning-based model for the regression of cloud-to-ground lightning location and peak current from time-domain waveforms of lightning-induced voltage measurements on overhead transmission lines is presented. A principal component analysis (PCA) procedure is applied for extracting significant features and decreasing the dimension of the input vector. Then, a shallow neural network is trained with the results of the PCA. The obtained results show that the proposed approach can be the base for a tool able to regress lighting location with an accuracy comparable to or even better than traditional methods [i.e., lightning location system (LLS)] and provide a peak current estimate more accurate than LLS and more actual and widespread than direct tower measurements (which are limited to a reduced number of recorded events in some specific regions). Such a tool would also have significant advantages in terms of costs, since it would not require a dedicated instrumentation.

Lightning represents one of the most critical issues for electrical infrastructure. In dealing with overhead distribution line systems, indirect lightning strikes can lead to induced voltages overcoming the critical flashover value of the line, thus damaging the insulators. The computation of lightning-induced voltages requires the modeling of the lightning current, the evaluation of the lightning electromagnetic fields and the solution of the field-to-line coupling equations. The numerical calculation of the lightning electromagnetic fields is time-consuming and is strongly dependent on the lightning channel modeling and soil properties. This article presents a review of the most widely adopted methods to calculate the lightning electromagnetic fields, starting from the classical formulation, which requires numerical integration, and highlighting the most effective approaches that have been developed to reduce computational effort. This is done first for the case of a perfectly conducting ground, then the available formulations to account for the ground finite conductivity are presented together with their possible implementations in both the frequency and time domains.

Lightning electromagnetic (EM) fields are typically computed by assuming the current propagation within a straight vertical channel of negligible cross section. However, it is well known that the lightning path is tortuous and this can significantly affect the radiated fields and the lightning-induced voltages on power lines. Lupò et al. (2000) proposed an analytical method evaluating the EM fields radiated by a tortuous channel assuming a step current source; to consider an arbitrary (realistic) current source, a slow-converging convolution integral was applied. The aim of this article is to reduce the computation time of such procedure by extending to the tortuous channel case the approach proposed by Brignone et al. (2021) for the evaluation of the lightning EM fields with arbitrary current source, originally developed for a straight vertical channel. The performances of the present method are evaluated by means of comparison with the solution proposed by Lupò et al. (2000), showing an excellent agreement with a significant reduction of the CPU effort. This in principle will allow the computation of lightning-induced overvoltages at both individual and statistical level without a prohibitive computational effort.