Counterfactual optimization for fault prevention in complex wind energy systems

TL;DR

This paper introduces a counterfactual optimization method to determine minimal control adjustments for fault prevention in complex wind energy systems, demonstrating significant economic savings and advancing the application of counterfactual analysis in energy management.

Contribution

It presents a novel counterfactual optimization approach tailored for complex energy systems, specifically offshore wind turbines, with practical validation and economic impact.

Findings

Achieved savings of approximately 3 million euros annually.

Successfully adapted the method to real-world data from an industrial partner.

Demonstrated the approach's effectiveness in fault prevention and system safety.

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.