标题： 故障预防的反事实优化在复杂风能系统中的应用 显示英文标题

标题： Counterfactual optimization for fault prevention in complex wind energy systems

摘要： 机器学习模型在企业中被越来越多地用于检测复杂系统中的故障和异常。 在这项工作中，我们进一步推进了这一方法：不仅检测异常，我们还旨在确定最优的控制策略，以最小的干扰将系统恢复到安全状态。 我们将这一挑战框架化为一个反事实问题：给定一个将系统状态分类为良好或异常的机器学习模型，我们的目标是确定对系统控制变量（即其当前状态）的最小调整，以使其恢复到良好状态。 为了实现这一点，我们利用了一个数学模型，在遵守系统特定约束的前提下找到最优的反事实解决方案。 值得注意的是，文献中大多数反事实分析都集中在个别案例上，其中一个人希望相对于分类器做出的决策（如贷款批准或医学诊断）改变自己的状态。 我们的工作解决了一个根本不同的挑战：优化复杂能源系统的反事实，特别是海上风力涡轮机油类型变压器。 这项应用不仅在新领域推进了反事实优化，还为该领域的更广泛研究开辟了途径。 我们对工业合作伙伴提供的真实数据进行的测试表明，我们的方法能够轻松适应用户偏好，并在典型农场中每年带来约300万欧元的节省。 显示英文摘要

摘要： 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.