A Graph Convolutional Network for Active Distribution System Anomaly Detection Considering Measurement Spatial-Temporal Correlations
The accuracy of distribution system state estimation may be significantly impacted by the existence of bad measure-ments and unexpected topology errors. This paper proposes a data-driven Graph Convolutional Network (GCN) for anomaly detection, including bad measurements and topology change events. Compared to many existing machine learning approaches, the proposed approach embeds both spatial-temporal measure-ment correlations, which allows us to detect and distinguish different anomalies. Numerical results carried out on the IEEE 37-node system demonstrate that the proposed-based method can obtain high accuracy in detecting bad data and topology changes as compared to other approaches, even in the presence of high PV penetrations.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; Eversource Energy
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 2281824
- Report Number(s):
- NREL/CP-5D00-88579; MainId:89358; UUID:5c06f3ff-2e3c-4a5d-8ef1-8d4b1f916071; MainAdminID:71578
- Resource Relation:
- Conference: Presented at the North American Power Symposium, 15-17 November 2023, Asheville, North Carolina
- Country of Publication:
- United States
- Language:
- English
Similar Records
Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction
Embedded, Real-Time, and Distributed Traveling Wave Fault Location Method Using Graph Convolutional Neural Networks