Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second. Because the machine-learning model they developed does not require annotated data on power grid anomalies for training, it would be easier to apply in real-world situations where high-quality, labeled datasets are often hard to come by. We show that we can automate this process and also learn patterns from the data using advanced machine-learning techniques,” says senior author Jie Chen, a research staff member and manager of the MIT-IBM Watson AI Lab.
Source: https://news.mit.edu/2022/artificial-intelligence-anomalies-data-0225
