Test Time Training for AC Power Flow Surrogates via Physics and Operational Constraint Refinement

• URL: http://arxiv.org/abs/2511.22343v1
• Date: Thu, 27 Nov 2025 11:27:54 GMT
• Title: Test Time Training for AC Power Flow Surrogates via Physics and Operational Constraint Refinement
• Authors: Panteleimon Dogoulis, Mohammad Iman Alizadeh, Sylvain Kubler, Maxime Cordy,
• Abstract summary: Power Flow calculation based on machine learning (ML) techniques offer significant computational advantages over traditional numerical methods.<n>This paper introduces a physics-informed test-time training framework that enhances the accuracy and feasibility of ML-based PF surrogates.
• Score: 11.02886935871606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Power Flow (PF) calculation based on machine learning (ML) techniques offer significant computational advantages over traditional numerical methods but often struggle to maintain full physical consistency. This paper introduces a physics-informed test-time training (PI-TTT) framework that enhances the accuracy and feasibility of ML-based PF surrogates by enforcing AC power flow equalities and operational constraints directly at inference time. The proposed method performs a lightweight self-supervised refinement of the surrogate outputs through few gradient-based updates, enabling local adaptation to unseen operating conditions without requiring labeled data. Extensive experiments on the IEEE 14-, 118-, and 300-bus systems and the PEGASE 1354-bus network show that PI-TTT reduces power flow residuals and operational constraint violations by one to two orders of magnitude compared with purely ML-based models, while preserving their computational advantage. The results demonstrate that PI-TTT provides fast, accurate, and physically reliable predictions, representing a promising direction for scalable and physics-consistent learning in power system analysis.

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