Abstract
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.
Original language | American English |
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Number of pages | 6 |
DOIs | |
State | Published - 2023 |
Event | North American Power Symposium - Asheville, NC Duration: 15 Oct 2023 → 17 Oct 2023 |
Conference
Conference | North American Power Symposium |
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City | Asheville, NC |
Period | 15/10/23 → 17/10/23 |
NREL Publication Number
- NREL/CP-5D00-88579
Keywords
- active distribution system
- anomaly detection
- graph convolutional network
- PVs
- topology change