Researchers at the University of Texas at Dallas and the University at Buffalo have developed an artificial intelligence model that could prevent power outages by rerouting electricity in milliseconds. The system, described as a “self-healing grid” technology, uses AI to detect and repair problems autonomously. The researchers, including Dr. Jie Zhang and Dr. Yulia Gel, demonstrated that their solution can identify alternative routes to transfer electricity before an outage occurs. The project, which also involves doctoral student Roshni Anna Jacob, was supported by the U.S. Office of Naval Research and the National Science Foundation.
AI Model for Preventing Power Outages
Researchers from the University of Texas at Dallas, in collaboration with engineers from the University at Buffalo, have developed an artificial intelligence (AI) model that could potentially prevent power outages. The model works by automatically rerouting electricity in milliseconds, a process that could take minutes to hours if done manually. This research, published in Nature Communications, 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 distribute electricity from power sources to consumers. The researchers demonstrated that their solution can automatically identify alternative routes to transfer electricity to users before an outage occurs. However, more research is needed before this system can be implemented, according to Dr. Jie Zhang, associate professor of mechanical engineering in the Erik Jonsson School of Engineering and Computer Science.
Graph Machine Learning in Power Distribution Networks
The researchers used technology that applies machine learning to graphs to map the complex relationships between entities that make up a power distribution network. This process, known as 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. Network topology may also play a critical role in applying AI to solve problems in other complex systems, such as critical infrastructure and ecosystems, according to Dr. Yulia Gel, professor of mathematical sciences in the School of Natural Sciences and Mathematics.
The researchers’ approach relies on reinforcement learning that makes the best decisions to achieve optimal results. If electricity is blocked due to line faults, 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.
Future Directions and Applications
After focusing on preventing outages, the researchers plan to develop similar technology to repair and restore the grid after a power disruption. This technology could potentially leverage power generators to supply electricity in a specific area, according to Roshni Anna Jacob, a UTD electrical engineering doctoral student and the paper’s co-first author.
The research was supported by the U.S. Office of Naval Research and the National Science Foundation. The potential applications of this technology extend beyond power distribution networks, with the possibility of solving problems in other complex systems such as critical infrastructure and ecosystems. The interdisciplinary nature of the project, combining expertise in power systems, mathematics, and machine learning, highlights the potential for AI to address complex, real-world problems.
Conclusion
The development of an AI model that can prevent power outages by automatically rerouting electricity is a significant step forward in the field of power distribution. While more research is needed before this system can be implemented, the potential benefits are clear. By leveraging machine learning and AI, researchers are able to address complex problems in power distribution, potentially preventing outages and ensuring a more reliable supply of electricity. This research not only has implications for the power industry but also for other complex systems, demonstrating the broad applicability of AI solutions.
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