Researchers developed an artificial intelligence model that could help electrical grids prevent power outages by automatically rerouting electricity in milliseconds. The approach is an early example of 'self-healing grid' technology, which uses AI to detect and repair problems such as outages autonomously and without human intervention when issues occur, such as storm-damaged power lines.
The North American grid is an extensive, complex network of transmission and distribution lines, generation facilities and transformers that distributes electricity from power sources to consumers. Zhang, who is co-corresponding author of the study, and his colleagues used technology that applies machine learning to graphs in order to map the complex relationships between entities that make up a power distribution network. Graph machine learning involves describing a network's topology, the way the various components are arranged in relation to each other and how electricity moves through the system.
The researchers' approach relies on reinforcement learning that makes the best decisions to achieve optimal results. Led by co-corresponding author Dr. Souma Chowdhury, associate professor of mechanical and aerospace engineering, University at Buffalo researchers focused on the reinforcement learning aspect of the project.
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