Counterfactual optimization for fault prevention in wind energy systems

Speaker: Martina Fischetti, tenure-track researcher at the University of Seville, Spain

About the speaker:

Martina Fischetti is a tenure-track researcher at the University of Seville, Spain. She holds M.Sc. degrees from the University of Padova (March 2014) and the University of Aalborg (June 2014) in Automation Engineering. In March 2018, she finished her Industrial PhD in OR at the Technical University of Denmark in collaboration with Vattenfall (the lead energy company in North Europe). Her PhD thesis was titled Mathematical Programming Models and Algorithms for Offshore Wind Park Design. Her PhD work on the optimization of wind farm design and cable routing has been awarded various international prizes, such as the Best Industrial PhD from Innovation Fund Denmark (2019), EURO Doctoral Dissertation Award (2019), Glover-Klingman Prize (2018), AIRO Best Application Paper award (2018), the Best Student Paper Award ICORES (2017), and finalist positions at the EURO Excellence in Practice award (2018) and the prestigious INFORMS Franz Edelman award (2019). She was also selected as a role model for young women in OR by the EURO WISDOM forum in 2021. After her PhD, she worked in industry (lead engineer in Vattenfall BA Wind, specializing in OR) and in government institutions (at the Joint Research Center of the European Commission in Seville, Spain, where she applied Operations Research to European transport challenges).

Abstract:

Machine Learning models are increasingly used in businesses to detect faults and anomalies in complex systems. In this work, we take this approach a step further: beyond merely detecting anomalies, we aim to identify the optimal control strategy that restores the system to a safe state with minimal disruption. We frame this challenge as a counterfactual problem: given a Machine Learning model that classifies system states as either “good” or “anomalous,” our goal is to determine the minimal adjustment to the system’s control variables (i.e., its current status) that is necessary to return it to the “good” state. To achieve this, we leverage a mathematical model that finds the optimal

counterfactual solution while respecting system-specific constraints. Notably, most counterfactual analysis in the literature focuses on individual cases where a person seeks to alter their status relative to a decision made by a classifier—such as for loan approval or medical diagnosis. Our work addresses a fundamentally different challenge: optimizing counterfactuals for a complex energy system, specifically an offshore wind turbine oil-type transformer. This application not only advances counterfactual optimization in a new domain but also opens avenues for broader research in this area. Our tests on real-world data provided by our industrial partner show that our methodology easily adapts to user preferences and brings savings in the order of 3 million euros per year in a typical farm.

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