Data-Driven Flow Control for Natural Gas Pipeline Networks: Optimizing via Anomaly Detection and Residual Weight...

The natural gas pipeline network has a complex topology with variable flow directions, and the supply demand relationships between nodes exhibit cyclical, fluctuating, and time-varying trends. Developing efficient, accurate, and fast intelligent control algorithms is crucial for optimizing the distribution of natural gas networks. Analyzing the operational data from a provincial network over three years revealed that abnormal flow data, such as supply interruptions due to incidents, early fulfillment of supply, and insufficient flow distribution, can cause deviations between the actual transmission volume and the planned transmission volume predicted by the uneven coefficient method. This leads to a decrease in distribution accuracy and, in severe cases, can result in distribution anomalies. Therefore, this paper proposes a data-driven flow control method for natural gas pipeline networks that optimizes flow distribution accuracy by identifying abnormal flow deviations and utilizing residual weight coefficients. First, based on the mathematical and statistical significance of weights, the residual weight coefficients used in this method and their calculation methods are defined. Second, the process of designing the residual weight coefficients is optimized through both manual intervention and automatic recognition control modes. Finally, the flow anomaly detection algorithm and the uneven coefficient method are applied to a real-time operational database for on-site validation, demonstrating the accuracy and advancement of this method. The results show that the distribution control algorithm based on flow anomaly detection can ensure that the distributed flow aligns with the plan by the fourth time step. Compared to the uneven coefficient method, this algorithm effectively reduces the distribution deviation rate, by up to 0.69. The application of this method enhances the matching accuracy between planned and actual transmission volumes across different time periods, ensuring the precision and response speed of natural gas pipeline network distribution control, thereby providing a foundational tool for the intelligent control of natural gas stations.