Using Big Data to Predict Power Outages

23 August 2017

Thousands of electric power customers in Sealy, Texas, near Houston, lost power in late May when severe storms swept through the area. Local utility CenterPoint Energy worked overnight to restore power to more than 48,000 customers, and aimed to restore service to all customers 24 hours after the storm. Besides damaging CenterPoint Energy's electric infrastructure and equipment, the storm may have damaged some customer-owned equipment, the utility said.

Power outages caused by trees and branches falling on electric distribution lines during severe weather can be costly to repair after the fact and mitigate through tree trimming programs.

Texas A&M University researchers have developed what they say is an intelligent model that can predict a potential vulnerability to utility assets and map where and when a possible outage may occur. The predictive feature allows trees in areas with the highest risk to be trimmed first.

The researchers used CenterPoint Energy’s utility data and presented a proof of concept to the company. Their next step is implementation of the model on CenterPoint’s database and environment. The company maintains the wires, poles, and electric infrastructure in a 5,000-square-mile electric service territory in the Houston metropolitan area.

Dr. Mladen Kezunovic, Regents Professor and holder of the Eugene E. Webb professorship in the Department of Electrical and Computer Engineering, along with graduate students Tatjana Dokic and Po-Chen Chen, developed the framework for a model that can predict weather hazards, vulnerability of electric grids and the economic impact of the potential damage.

By analyzing the impact of a potential vulnerability and weather impacts on power system outages, the researchers can predict where and when outages may occur. Kezunovic says that any kind of environmental data that has some relevance to the power system can be fed into this prediction framework.

Data such as a utility company’s operational records, weather forecasts, altitude, and vegetation around the power systems can be used to customize the model.

The research team says the model is flexible and can process a variety of data despite differing formats and data sources. Every source of data and its presentation is different and multifaceted. Based on the goals, the reseachers select a large amount of input data from several sources and perform a risk analysis.

Three-Part Process

The researchers say their method is a three-part process. First, they investigate the probability of a potential hazard, such as severe weather. Next, they assess the vulnerability of the utility assets by taking the weather probability and predicting its impact on the assets. Third, they evaluate the impact of certain events and calculate costs of reliability indices and maintenance, replacement and repair.

“Overall the risk analysis helps predict the probability of events happening in the near future and then adds the financial impact allowing development of an optimal action plan for the utility operators to execute,” says Chen.

CenterPoint Energy also plans to use unmanned aerial systems (UAS) as part of its Emergency Operating Plan (EOP). Drones could help expedite the company's ability to assess damage to its electric transmission and distribution system following a hurricane.

"We had the opportunity to test drone technology following severe weather in Sealy, Texas, and see potential for drones to play a key role in storm and disaster response," said Kenny Mercado, senior vice president of Electric Operations for CenterPoint Energy in a statement.

The Texas A&M research was supported by CenterPoint Energy, the National Science Foundation Center for Ultra-Wide Area Resilient Electric Energy Transmission Networks, and in part by NSF Power Systems Engineering Research Center and NSF Smart Grid Big Data Spoke grants. Read more about the research in the IEEE Transactions on Smart Grid journal.

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