Design of Distribution Network Operation and Maintenance Mode based on Deep Learning

With the increasing scale and complexity of power distribution networks, traditional operational maintenance approaches are no longer able to meet the requirements of stable, reliable, and efficient management. The design of power distribution network operational maintenance models based on deep learning has become a hot topic of research. This article will discuss how to utilize deep learning technology to design operational maintenance models for power distribution networks, aiming to improve their reliability and operational efficiency. Specifically, deep learning can be applied in fault detection and diagnosis, load forecasting, and equipment status monitoring in power distribution networks. By leveraging these applications, the power distribution system can more accurately detect and diagnose faults, predict load variations, and monitor equipment statuses, thereby enhancing the network’s stability and efficiency. The results indicate that the deep learning-based power distribution network operation and maintenance model achieves an accuracy rate of over 95%, significantly improving the reliability and operational efficiency of the network. It outperforms traditional particle swarm algorithms, providing important references and guidance for the practical maintenance of power distribution networks.