Research on Intelligent Operation and Maintenance Decision Support for Electrical Systems by Convergence of Artificial Intelligence and Edge Computing

Aiming at the problems of poor real-time and low decision-making efficiency that exist in the operation and maintenance of traditional electrical systems, this thesis conducts an in-depth study on the application of the fusion technology of artificial intelligence and edge computing in the decision support of intelligent operation and maintenance of electrical systems. By analyzing the synergistic mechanism of artificial intelligence algorithms and edge computing architecture, the fusion model is constructed to realize real-time collection, intelligent analysis and accurate decision-making of electrical equipment data. Combined with practical case verification, the fusion technology can increase the accuracy of electrical system fault warning to 92%, shorten the response time of operation and maintenance decision-making by 60%, effectively reduce the cost of operation and maintenance, improve the reliability and stability of the electrical system, and provide theoretical and technical support for the intelligent operation and maintenance of the electrical system.