A research team at Argonne National Laboratory developed a novel approach to helping system operators understand how to better control power systems with the help of artificial intelligence. Scientists at the U.S. Department of Energy's Argonne National Laboratory (ANL) have created a new artificial intelligence (AI) technique to help operators understand how to better handle power systems. Their single decision-making neural network model manages both static and dynamic processes with a relatively high level of accuracy, and could augment the resilience of the U.S. power grid. ANL's Yichen Zhang said, "A neural network can create a map between a specific input and a specific output. If I know the conditions we start with and those we end with, I can use neural networks to figure out how those conditions map to each other."

With a new neural network, lab scientists helped create new formulas that could bridge a power system's static and dynamic features -- a difficult feat. America's power grid system is not only large but dynamic, which makes it especially challenging to manage. Human operators know how to maintain systems when conditions are static.