MIT-IBM Watson AI Lab Tackles Power Grid Failures with AI

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.