Using big data to prevent large-scale power outages
An associate professor in NJIT’s Martin Tuchman School of Management, Maggie Cheng is leveraging the power of big data analytics to create a “smart” electric power grid.
Her goal is to help prevent large-scale power outages by using real-time data to detect, diagnose and interpret changes in the network.
The significance for society and for the power industry is profound. The Northeast Blackout of 2003 — the biggest in North American history — began as a string of local events in Ohio that spiraled into a massive problem affecting 50 million people in eight U.S. states and the Canadian province of Ontario. Its economic impact in terms of losses to U.S. workers, consumers and taxpayers was estimated at $6.4 billion. Since then, an increased reliance on variable energy supply sources has made system understanding and situational awareness of the complex energy system even more challenging.
Dr. Cheng’s work on real-time anomaly-detection technology using measurement data would allow control centers to take appropriate actions in response to alerts of faults or disturbances, and prevent minor events from turning into major power outages.