From: Texas A&M University
Published July 27, 2017 11:29 AM

Researchers develop model to predict and prevent power outages using big data

High-speed winds during a thunderstorm may cause trees around an electric grid to crash into the distribution system feeders causing an outage in that area. Currently, most utility companies diminish such accidents by scheduling regular tree-trimming operations. This effort is costly and is based on a rotational approach to different service areas, which may take months and sometimes years before all trees are trimmed.

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

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, have 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 can occur. Predicting an optimal tree trimming schedule that would minimize the risk of vegetation-related outages is only one of the applications.

Read more at Texas A&M University

Image: This map is generated by the model to represent potential vulnerabilities in the power grid during severe weather in a specific location. (Credit: Texas A&M University)

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