Analysis Method for Distribution Network Operation Status Based on Middleware Architecture and Deep Learning

A distribution network operation status analysis method based on middleware architecture and deep learning is proposed to address the problem of numerous operating parameters in the distribution network and the need to improve the accuracy of operation status monitoring. Firstly, a distribution network operation analysis model is constructed based on the middleware architecture, and comprehensive analysis of operation data is achieved through hierarchical collaboration. Then, the running data is decomposed into low-frequency and high-frequency parts using wavelet packet decomposition for further analysis. Finally, a dual branch deep learning network was designed for both state monitoring and prediction, utilizing deep recurrent neural networks to output state prediction results. Based on the selected dataset, experimental analysis was conducted on the proposed method, and the results showed that its analysis accuracy and time were about 98% and 5 seconds, which can meet the real-time analysis requirements of distribution network operation status.