AC Power Flow Contingency Analysis Using a Single Deep Neural Network

Abstract: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 neural network (DNN), we derive sufficient conditions that guarantee convergence and develop semidefinite programming (SDP) formulations to certify these conditions for a given DNN. Numerical tests on the IEEE 118-bus system demonstrate that the proposed SDP formulations are tight, that the certified conditions hold for all tested contingencies, and that the resulting method produces accurate post-contingency state estimates within only a few iterations.

Systems and Control (eess.SY); Machine Learning (cs.LG)

arXiv:2609.30859 [eess.SY]

(or arXiv:2609.30859v1 [eess.SY] for this version)

https://doi.org/10.48550/arXiv.2609.30859 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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