An optimization model that minimally adjusts wind turbine control setpoints to flip an ML anomaly classifier from anomalous to good is demonstrated on real transformer data, with an extrapolated savings estimate of roughly 3 million euros per farm per year.
Mathematical optimization modelling for group counterfactual explanations
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Counterfactual optimization for fault prevention in complex wind energy systems
An optimization model that minimally adjusts wind turbine control setpoints to flip an ML anomaly classifier from anomalous to good is demonstrated on real transformer data, with an extrapolated savings estimate of roughly 3 million euros per farm per year.