Storm outage modeling for an electric distribution network in Northeastern USA

Abstract

The interaction of severe weather, overhead lines and surrounding trees is the leading cause of outages to electric distribution networks in forested areas. In this paper, we show how utility-specific infrastructure and land cover data, aggregated around overhead lines, can improve outage predictions for Eversource Energy (formerly Connecticut Light and Power), the largest electric utility in Connecticut. Eighty-nine storms from different seasons (cold weather, warm weather, transition months) in the period 2005-2014, representing varying types (thunderstorms, blizzards, nor'easters, hurricanes) and outage severity, were simulated using the Weather Research and Forecasting (WRF) atmospheric model. WRF simulations were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This framework could be used for predicting outages to other types of critical infrastructure networks with benefits for emergency-preparedness functions in terms of equipment staging and resource allocation.

Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas

“…Outages were defined as individual locations that require a two-man restoration crew to manually intervene and restore power, which are recorded at the nearest upstream isolating device ("asset") to a fault (i.e., downed powerline, broken pole, fuses, reclosers, switches, and transformers). The count of isolating devices per 1 km grid cell were included in the model to represent the amount of infrastructure in a given area, which has been shown to be an important offset in recent studies (e.g., the count of outages per grid cell cannot exceed the count of isolating devices within a grid cell) [3,4]. Furthermore, no dynamics of the actual power grid infrastructure have been included in our study, as each grid cell is treated as spatially independent.…”

“…We selected this model for its ability to discern nonlinear patterns [38]; a feature that has proven useful in a variety of remote sensing studies for change detection and extraction of damaged areas, where the objective is to assess substantial changes between single pixel-based elements [39][40][41]. Other types of machine learning models such as random forest [3,4] and Bayesian additive regression trees [2,4] have been used to relate weather and geographic data to outages, but we believe that this is the first attempt at synergistically combining electrical infrastructure, population, and satellite observations of NTL to quantitatively estimate outages needing repair. To prevent overfitting and ensure model accuracy, we performed a 20-fold cross-validation such that 95% of the data was used for training and 5% of the data was used as an independent validation (without replacement).…”

Nonparametric Tree‐Based Predictive Modeling of Storm Outages on an Electric Distribution Network

“…Aiming to assist the largest utility company in Connecticut, Eversource Energy, in prestorm decision making, we investigated two models and compared their predictions of spatial outage patterns and their ability to perform statistical inference. This study builds on our previous research that investigated the use of different model forcing data and methods for predicting power outages in Connecticut . Prediction intervals of model estimates are as important for risk management as point estimations of storm outages; a point estimate only provides a single value at each location to describe the predicted storm outages, while prediction intervals provide a characterization of the uncertainty associated with the prediction.…”

Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution

“…Prior approaches account for randomness of failures but rarely dynamics [6] [9] [13]- [15]. Models for failures are widely studied in computer-communication, e.g., finite state Markov process [16] and reliability of other multi-component systems [17] [18].…”