Digital Twin–Enabled Monitoring and Modeling of Substation Secondary Systems in Distributed Generation Networks

Abstract

In order to cope with the challenges brought by the large-scale access of distributed energy resources (DER) to the operation and monitoring of substation secondary systems, this paper proposes a digital twin (DT) modeling and operation monitoring framework that integrates dynamic Bayesian network (DBN) and cloud-edge cooperative architecture. The framework constructs a layered system of “cloud-edge-end” collaboration: the physical layer realizes high-frequency (≥50 Hz/100 Hz) real-time data acquisition and redundant transmission based on standard protocols such as IEC 61850/Modbus, etc.; the edge layer is in charge of low-latency (<20 ms) data preprocessing, filtering, anomaly detection, and preliminary state estimation; the cloud core layer is responsible for low latency (<20 ms) data preprocessing, filtering, anomaly detection, and preliminary state estimation; the cloud core layer is responsible for low-latency data preprocessing, filtering, anomaly detection, and preliminary state estimation. The edge layer is responsible for low-latency (<20 ms) data preprocessing, filtering, anomaly detection, and preliminary state estimation; the cloud core layer integrates the untraceable Kalman filter (UKF) algorithm for dynamic state estimation, and combines with the DBN to model the temporal dependency of the heterogeneous data from multiple sources, realizing the high-precision state tracking and fault prediction in the whole life cycle. Innovatively, the study designed a dual-track algorithm: the adaptive UKF is used to handle system nonlinearity and noise uncertainty and update the state estimation in real time (error ≤ 4.8%); the DBN online incremental learning mechanism (e.g., online EM algorithm) is utilized to dynamically update the conditional probability table and reconfigure the network structure, if necessary, to adapt to DER fluctuations and sudden failure modes. Experimental validation shows that the system has a fault warning accuracy of 96.5% in simulated fault scenarios, an average trigger delay of 150 ms, a state estimation response delay of 100–500 ms, and various key performance indicators (data sampling frequency, transmission delay, edge processing capability, storage capacity, etc.) meet or exceed the design requirements. The framework effectively improves the design accuracy, operation transparency and fault disposal efficiency of substation secondary system in distributed energy environment, and provides a scalable and comprehensive solution for the refined operation and maintenance of smart grid.