Real-time monitoring and optimization of SPN networks in distribution grid communication based on big data analysis

With the rapid integration of wind and solar energy into distribution grids, the high-frequency fluctuations of renewable energy output pose significant challenges to the Software-Defined Power Communication Converged Network (SPN), particularly in terms of dynamic communication load surges and inefficient topology adaptation. This study addresses these critical issues by leveraging the State Grid Wind-Solar Competition Dataset to develop a data-driven SPN monitoring and optimization framework. Key innovations include three core components: (1) an attention-enhanced long short-term memory (LSTM) model fused with Variational Mode Decomposition (VMD) for SPN communication delay prediction, which prioritizes high-correlation features from the dataset and achieves a mean absolute error (MAE) of 8.2 ± 0.3 ms for 1-hour-ahead prediction (the predicted target is explicitly defined as SPN communication delay in milliseconds, and the communication load index L t (in Mbps) is a key influencing feature); (2) a wind-solar fluctuation grade-driven SPN topology self-optimization method, which classifies renewable fluctuations into five grades via K-Means clustering and dynamically adjusts routing weights considering Flexible Ethernet (FlexE) hard slicing, reducing average communication latency by 62.0 ± 3.2% and packet loss rate from 5.8 ± 0.4% to 0.3 ± 0.1%; (3) a cross-climate SPN communication fingerprint library integrating seasonal features, which integrates statistical characteristics of “wind-solar features-communication load-seasonal fluctuations” from multi-climate sites, shortening new site debugging time by 97.9 ± 0.5% (from 24 to 0.5 h) and reducing post-matching latency by 26.8 ± 1.3%. Comprehensive experimental validation (10 random seed runs) demonstrates that the proposed framework effectively bridges the gap between renewable energy dynamics and power communication network operations.