Wind turbine power curve models translate ambient conditions into turbine power output. They are essential for energy yield prediction and turbine performance monitoring. In recent years, increasingly complex machine learning methods have become state-of-the-art for this task. Nevertheless, they frequently encounter criticism due to their apparent lack of transparency, which raises concerns regarding their performance in non-stationary environments, such as those faced by wind turbines. We, therefore, introduce an explainable artificial intelligence (XAI) framework to investigate and validate strategies learned by data-driven power curve models from operational wind turbine data. With the help of simple, physics-informed baseline models it enables an automated evaluation of machine learning models beyond standard error metrics. Alongside this novel tool, we present its efficacy for a more informed model selection. We show, for instance, that learned strategies can be meaningful indicators for a model's generalization ability in addition to test set errors, especially when only little data is available. Moreover, the approach facilitates an understanding of how decisions along the machine learning pipeline, such as data selection, pre-processing, or training parameters, affect learned strategies. In a practical example, we demonstrate the framework's utilisation to obtain more physically meaningful models, a prerequisite not only for robustness but also for insights into turbine operation by domain experts. The latter, we demonstrate in the context of wind turbine performance monitoring. Alongside this paper, we publish a Python implementation of the presented framework and hope this can guide researchers and practitioners alike toward training, selecting and utilizing more transparent and robust data-driven wind turbine power curve models.

翻译：风力发电机功率曲线模型将环境条件转化为涡轮发电机输出功率，对能量产量预测和涡轮性能监测至关重要。近年来，日益复杂的机器学习方法已成为该任务的最先进技术。然而，这些方法常因缺乏透明度而受到批评，这引发了对其在非平稳环境（如风力发电机所面临的环境）中性能的担忧。为此，我们引入了一种可解释人工智能（XAI）框架，用于研究并验证数据驱动功率曲线模型从运行风力发电机数据中学习的策略。借助简单的、基于物理原理的基线模型，该框架能够超越标准误差指标，自动评估机器学习模型。除这一新颖工具外，我们还展示了其在更明智模型选择中的有效性。例如，我们证明学习策略除了测试集误差外，还可作为模型泛化能力的有意义指标，尤其是在数据量较少的情况下。此外，该方法有助于理解机器学习流程中的决策（如数据选择、预处理或训练参数）如何影响学习策略。通过实际案例，我们展示了该框架在获得更符合物理意义的模型方面的应用，这不仅是鲁棒性的前提，也是领域专家洞察涡轮运行的先决条件。后者我们以风力发电机性能监测为例进行了演示。本文还发布了所提出框架的Python实现，希望这能引导研究人员和实践者训练、选择并利用更透明、更鲁棒的数据驱动风力发电机功率曲线模型。