University of Texas at Dallas researchers have developed an artificial intelligence (AI) model to help electrical grids prevent power outages by automatically rerouting electricity in milliseconds.
The UT Dallas researchers, who collaborated with engineers at the University at Buffalo in New York, demonstrated the automated system in a study published online in Nature Communications. The work was supported by the U.S. Office of Naval Research and the National Science Foundation.
The study was built on a 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 researchers demonstrated that their solution is capable of automatically identifying alternative routes to transfer electricity to users before an outage occurs.
While AI can automatically reroute electrical flow in milliseconds, current human-controlled processes to determine alternate paths take longer duration.
Dr. Jie Zhang, associate professor of mechanical engineering in the Erik Jonsson School of Engineering and Computer Science and his colleagues used technology applying machine learning to graphs to map the complex relationships between entities making 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 electricity movement through the system.
According to Dr. Yulia Gel, professor of mathematical sciences in the School of Natural Sciences and Mathematics, network topology is also important in applying AI to solve problems in other complex systems, such as critical infrastructure and ecosystems.
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.
Roshni Anna Jacob, a UTD electrical engineering doctoral student and the paper’s co-first author stated that the system is able to reconfigure using switches and draw power from available sources in close proximity, such as from large-scale solar panels or batteries on a university campus or business, if electricity is blocked due to line faults.
The researchers will also aim to develop similar technology to repair and restore the grid after a power disruption.
