Our research team unveils a machine-learning framework that detects power-quality disturbances in real time across distribution feeders.
The Power Grid Research and Innovation Institute has published new results demonstrating a deep-learning framework capable of classifying voltage sags, swells and harmonic events with over 98% accuracy. The work supports utilities in moving from reactive to predictive power-quality management.