Predicting Lightning-Related Outages in Power Distribution Systems: A Statistical Approach

Abstract

This paper presents a novel data-driven approach for predicting lightning-related outages that occur in power distribution systems on a daily basis. In order to develop an approach that is able to successfully fulfill this objective, there are two main challenges that ought to be addressed. The first challenge is to define the extent of the target area. An unsupervised machine learning approach is proposed to overcome this difficulty. The second challenge is to adequately identify characteristics of lightning-related outages and to explore the relationship between these outages and weather-related variables (thunderstorm events). In this paper, these outages are clustered into a few manageable groups. Then, a probabilistic model is presented to estimate the likelihood of each group of outages. Finally, a machine learning classification algorithm that can handle the imbalanced problem is developed to predict what group will the outage belong to on a specific day in a specific area of the system under study. Actual outage data, obtained from a major utility in the U.S., in addition to radar weather forecast data are utilized to build the proposed approach. Also, three case studies are provided to show several issues associated with predicting lightning-related outages, and to demonstrate how the proposed approach can address those problems adequately.

Forecasting weather‐related power outages using weighted logistic regression

“…Many challenges associated with forecasting outages at the substation level are highlighted in ref. [17]. For this case study, we select five substations within the service area of the utility and implement the proposed model M 8 and observe its performance.…”

“…Major weather events such as storms and hurricanes can cause widespread outages, lasting several days. Consequently, a significant number of noteworthy studies have looked into forecasting power outages caused by particular weather events, such as storms [13,14], hurricanes [15,16], and lightning [17]. The authors in ref.…”

“…In ref. [17], a weighted logistic regression model for lightning-related outage forecasts is presented. The weights of the model are fine-tuned by a process of trial-and-error to compensate for the imbalance in the data.…”

“…The various challenges associated with forecasting outages at the substation level are highlighted in refs. [17,26]. As a result, among the outage forecasting literature, only a few papers take into account forecasting outages at the substation level [13,24].…”

A Method to Obtain Lightning Peak Current in Indonesia

“…Lightning strikes can result in the destruction of building structures, damage to electrical systems [1], tree damage [2], explosions in tank farms [3,4], equipment damage [5], loss of life [6,7], and various other negative impacts [8][9][10][11][12][13]. Statistics on oil tank failures reveal that from 1995 to 2021, lightning in Indonesia triggered an oil tank fire every single year [14].…”