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This machine learning pipeline integrates physics-informed constraints with solar-wind data to generate location-specific risk estimates for over 66,000 US substations. By predicting geomagnetic storm impacts 30–60 minutes in advance, the system enables grid operators to mitigate equipment damage and reduce operational risks during extreme space-weather events.

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Forecasting space weather risks on power grids

Microsoft ResearchSeptember 30, 20266 min read

Extreme space-weather events can damage power systems on Earth and degrade GPS accuracy and satellite operations. A new machine learning system can predict where damage is likely to occur 30-60 minutes before a storm arrives. The post Forecasting space weather risks on power grids appeared first on Microsoft Research.

End-to-end forecasting: A machine learning pipeline uses forecast-time solar-wind information to generate location-specific risk estimates for 66,935 substations in the continental United States. Physics and place: The system combines Auroral Electrojet (AE) and Disturbance Storm Time (Dst) forecasts with local latitude, geology, and ground conductivity. Advance warning: The pipeline detected nearly 80% of major space-weather events during the evaluation period and can warn grid operators 30 to 60 minutes before a specific risk appears. During the May 2024 geomagnetic storm, utilities across North America prepared for possible impacts as auroras extended far beyond their usual range. The storm would degrade GPS accuracy and satellite operations, impacting farm operations in North…

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