AC Power Flow Contingency Analysis Using a Single Deep Neural Network
Contingency analysis using the AC power flow (AC-PF) model is a critical tool for accurate grid security assessment, but its computational burden increases with the number of operating scenarios and outage configurations to evaluate. Recent ML-based approaches typically require outage-specific training data, leading to offline training costs that scale with the number of contingencies. This work proposes a framework that reuses a single ML model trained solely on basecase AC-PF data to estimate post-contingency operating states under arbitrary single-line outages. The proposed approach formulates post-contingency state prediction as a fixed-point iteration. If the ML model is a deep neura...
| Authors | Md Obaidur Rahman, Junjie Qin, Vassilis Kekatos |
|---|---|
| Primary category | eess.SY |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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