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MIT-IBM Watson AI Lab Tackles Power Grid Failures with AI in Datanami By Jaime Hampton
Next time your power stays on during a severe weather event, you may have a machine learning model to thank.
Researchers at the MIT-IBM Watson AI Lab are using artificial intelligence to solve power grid failures. The manager of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine learning model that works to analyze data collected from hundreds of thousands of sensors located across the U.S. power grid.
The sensors, components of what is known as synchrophasor technology, compile vast amounts of real-time data related to electric current and voltage in order to monitor the health of the grid and locate anomalies that could cause outages.
Synchrophasor analysis requires intensive computational resources due to the size and real-time nature of the data streams the sensors produce. There can be difficulty with quickly extracting data for anomaly detection, or the “task of identifying unusual samples that significantly deviate from the majority of the data instances,” as defined in the researchers’ paper.
The ML model can be trained without annotated data on power grid anomalies, which is advantageous because much of the data collected by the sensors is unstructured.
“In the case of a power grid, people have tried to capture the data using statistics and then define detection rules with domain knowledge to say that, for example, if the voltage surges by a certain percentage, then the grid operator should be alerted. Such rule-based systems, even empowered by statistical data analysis, require a lot of labor and expertise. We show that we can automate this process and also learn patterns from the data using advanced machine-learning techniques,” said Chen in an MIT News article. .... '
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