Research and Application of Deep Learning Algorithm Based on Multi-Source Data Fusion of Power Grid And Communication Network
Li Li, Wei Zhou, Liang Ma, Yingjie Jiang, Chao Sun, Pu Zhang · 2024
Multi-source data fusion plays a very important role in improving the intelligent level of power grid operation, but there is a problem that multi-source data fusion is not ideal. The previous operation mode could not solve the problem of data fusion in the intelligent operation of the power grid, and the processing was inaccurate. Therefore, this paper proposes a deep learning algorithm for multi-source data fusion analysis. Firstly, the signal processing and estimation theory are used to integrate the power grid communication data, and the indicators are carried out according to the requirements of multi-source data fusion Partitioning to reduce interference factors in multi-source data fusion. Then, signal processing and estimation theory intelligently process power grid communication to form an intelligent processing scheme and fuse multi-source data The results were comprehensively analyzed. MATLAB simulation shows that under the condition of certain processing standards, deep learning algorithms intelligently process power grid communication accuracy and multi-source data fusion time are better than previous operating modes.