[2510.25147] Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk

Computer Science > Machine Learning arXiv:2510.25147 (cs) [Submitted on 29 Oct 2025 (v1), last revised 1 Apr 2026 (this version, v2)] Title:Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk Authors:Weimin Huang, Ryan Piansky, Bistra Dilkina, Daniel K. Molzahn View a PDF of the paper titled Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk, by Weimin Huang and 3 other authors View PDF HTML (experimental) Abstract:To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas. The Optimal Power Shutoff (OPS) problem optimizes line energization statuses to manage wildfire ignition risks through de-energizations while reducing load shedding. OPS problems are computationally challenging Mixed-Integer Linear Programs (MILPs) that must be solved rapidly and frequently in operational settings. For a particular power system, OPS instances share a common structure with varying parameters related to wildfire risks, loads, and renewable generation. This motivates the use of Machine Learning (ML) for solving OPS problems by exploiting shared patterns across instances. In this paper, we develop an ML-guided framework that quickly produces high-quality de-energization decisions by extending existing ML-guided MILP solution methods while integrating domain knowledge on the number of energized and de-energized lines. Results on a large-scale realistic California-based synthetic ...