Optimization Design of Power Grid Intelligent Monitoring System Based on Deep Learning

The intelligent monitoring system of the power grid is a critical support for ensuring the safe and stable operation of the grid, and enhancing its intelligence level is of great significance. Addressing issues such as low fault recognition accuracy and slow response speed in traditional monitoring systems, this paper designs an intelligent monitoring system for power grids based on deep learning. A layered architecture is adopted to design the system framework, and a CNN-LSTM hybrid network is constructed for fault diagnosis. Additionally, a distributed data processing platform is built on the Spark framework. Experimental results demonstrate that the proposed system achieves high fault recognition accuracy and data processing capabilities, significantly shortens warning times, reduces deployment and maintenance costs, and provides a feasible technical solution for smart grid development.