<p>Researchers from the University of Texas at Dallas (UT Dallas) in collaboration with engineers from the University at Buffalo in New York have developed an AI-backed system which can repair electric grid automatically. The system can reroute electricity within milliseconds. </p><p>According to a statement by the university, this AI model aims to detect and repair issues in electrical grids autonomously, without requiring human intervention. </p><p>According to an <a href="https://interestingengineering.com/energy/ai-model-repairs-electric-grid-automatically" rel="nofollow">article</a> by <em>Interesting Engineering, </em>Dr Jie Zhang, associate professor of mechanical engineering at the Erik Jonsson School of Engineering and Computer Science, stated, “Our goal is to find the optimal path to send power to the majority of users as quickly as possible. But more research is needed before this system can be implemented.”</p><p>The new system developed by the researchers is mainly focused on the North American electrical grid system.</p><p>The AI powered system falls under the category of "self-healing smart grids". Researchers expect the technology to reduce power disruptions during outages.</p><p>The article further states that the system uses a machine learning technique known as graph reinforcement learning which analyzes and manages the complex power grid network. </p>.That is not dead which can eternal lie...: A 'digital afterlife' is no longer science-fiction, so how do we navigate the risks?.<p>The study was published on June 4 in a journal called <em>Nature Communications. </em></p><p><em>Interesting Engineering </em>notes that the system was tested on different network setups and it demonstrated near-optimal real-time performance. The system also significantly reduced energy loss during outages in various circumstances.</p><p>“In this interdisciplinary project, by leveraging our team expertise in power systems, mathematics, and machine learning, we explored how we can systematically describe various interdependencies in the distribution systems using graph abstractions,” the article quoted one of the co-authors of the study Dr Yulia Gel.</p>
<p>Researchers from the University of Texas at Dallas (UT Dallas) in collaboration with engineers from the University at Buffalo in New York have developed an AI-backed system which can repair electric grid automatically. The system can reroute electricity within milliseconds. </p><p>According to a statement by the university, this AI model aims to detect and repair issues in electrical grids autonomously, without requiring human intervention. </p><p>According to an <a href="https://interestingengineering.com/energy/ai-model-repairs-electric-grid-automatically" rel="nofollow">article</a> by <em>Interesting Engineering, </em>Dr Jie Zhang, associate professor of mechanical engineering at the Erik Jonsson School of Engineering and Computer Science, stated, “Our goal is to find the optimal path to send power to the majority of users as quickly as possible. But more research is needed before this system can be implemented.”</p><p>The new system developed by the researchers is mainly focused on the North American electrical grid system.</p><p>The AI powered system falls under the category of "self-healing smart grids". Researchers expect the technology to reduce power disruptions during outages.</p><p>The article further states that the system uses a machine learning technique known as graph reinforcement learning which analyzes and manages the complex power grid network. </p>.That is not dead which can eternal lie...: A 'digital afterlife' is no longer science-fiction, so how do we navigate the risks?.<p>The study was published on June 4 in a journal called <em>Nature Communications. </em></p><p><em>Interesting Engineering </em>notes that the system was tested on different network setups and it demonstrated near-optimal real-time performance. The system also significantly reduced energy loss during outages in various circumstances.</p><p>“In this interdisciplinary project, by leveraging our team expertise in power systems, mathematics, and machine learning, we explored how we can systematically describe various interdependencies in the distribution systems using graph abstractions,” the article quoted one of the co-authors of the study Dr Yulia Gel.</p>