Using Big Data to Predict and Prevent Power Outages
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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.
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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