Researchers proposed a new approach of combining high-performance quantum computing with artificial intelligence to decipher an electric grid’s faults.
Energy grid faults, in some cases, can turn into giant problems. Artificial Intelligence (AI) could be a solution to these faults. Quantum computing and AI can learn to decipher an electric grid’s problematic quirks and solve system problems quickly. According to the researchers from Cornell University, quantum computing combined with artificial intelligence could rapidly diagnose trouble and find solutions in tiny splits of seconds.
“Energy power system failures are an old problem and we are still using classic computational methods to resolve them,” said Fengqi You, the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering in the College of Engineering. “Today’s power systems can benefit from AI and the computational power of quantum computing, so power systems can be stable and reliable.”
The researchers proposed a novel hybrid solution by creating a quantum-computing-based “intelligent system” approach to build a fault-diagnosis framework to accurately find problems in electrical power systems.
In the paper published in Applied Energy journal, the researchers demonstrated the efficacy and scalability in a large-scale IEEE test electrical power system. They found that a quantum computing-based deep-learning approach can be scaled efficiently for a quick diagnosis in larger power systems without loss of performance.
“We cannot afford for grids to go down,” Ajagekar, co-author of the paper, said. “That’s why fast fault diagnosis in electrical power systems is very important. Today’s systems have sensors, but even they’re not good enough now. We need efficiency. It’s very expensive to wait minutes, hours or days.”
“Electrical power systems are the backbone of our modern world,” said You, a faculty fellow with the Cornell Atkinson Center for Sustainability. “The marriage of quantum technologies and AI could make a difference in our daily life.”
