Application of distribution network monitoring information automatic verification platform driven by artificial intelligence in improving acceptance testing and power grid operation and maintenance management efficiency

Abstract In the construction, operation and maintenance of modern power grids, the monitoring, acceptance testing, and operation and maintenance of distribution networks have always been key links. With the rapid development of technologies such as smart grids, the Internet of Things and artificial intelligence (AI), how to enhance the efficiency of distribution network acceptance testing and operation and maintenance management has become an urgent issue to be addressed. However, traditional distribution network acceptance testing and operation and maintenance management rely on manual operations, which are inefficient, error-prone, and lack data integration and intelligent support. In response to the above problems, this paper studies and designs an automatic verification platform for distribution network monitoring information based on AI. The platform first collects and integrates distribution network information, performs preliminary data processing, and then realizes automatic verification and anomaly detection of the monitored platform data through the long short-term memory (LSTM) network model. In addition, the particle swarm optimization (PSO) algorithm is utilized to optimize the task scheduling of the platform, and the greedy algorithm is used to optimize the platform’s load balancing, aiming to enhance the platform’s processing efficiency, response speed, and resource allocation capability. After acceptance testing, the outcomes demonstrate that the platform designed in this paper has good verification consistency for the distribution network, and the verification consistency of various major equipment remains above 90 %. It can detect abnormalities and give feedback for different monitoring points in different distribution network areas. The traditional operation and maintenance solution is compared with the platform designed in this paper regarding operation and maintenance management efficiency. The results show that in the seven key indicators of operation and maintenance management efficiency, the platform designed in this paper has shown obvious advantages, providing a practical solution for the intelligent management of the distribution network and contributing to the development of automation, intelligence, and efficiency of power grid operation and maintenance.