Accurate wind turbine power curve models, which translate ambient conditions into turbine power output, are crucial for wind energy to scale and fulfill its proposed role in the global energy transition. While machine learning (ML) methods have shown significant advantages over parametric, physics-informed approaches, they are often criticised for being opaque 'black boxes', which hinders their application in practice. We apply Shapley values, a popular explainable artificial intelligence (XAI) method, and the latest findings from XAI for regression models, to uncover the strategies ML models have learned from operational wind turbine data. Our findings reveal that the trend towards ever larger model architectures, driven by a focus on test set performance, can result in physically implausible model strategies. Therefore, we call for a more prominent role of XAI methods in model selection. Moreover, we propose a practical approach to utilize explanations for root cause analysis in the context of wind turbine performance monitoring. This can help to reduce downtime and increase the utilization of turbines in the field.

翻译：精确的风力发电机功率曲线模型——即将环境条件转换为涡轮发电功率输出的模型——对于风能规模化发展及其在全球能源转型中发挥预期作用至关重要。尽管机器学习方法相较基于物理的参数化方法展现出显著优势，但其常因被视为不透明的"黑箱模型"而受到批评，这阻碍了其在实践中的应用。本研究采用沙普利值（一种流行的可解释人工智能方法）以及XAI回归模型的最新研究成果，揭示机器学习模型从运行风力发电机数据中习得的策略。研究发现表明，追求测试集性能所驱动的模型架构日益大型化趋势，可能导致物理上不可信的策略。因此，我们呼吁在模型选择中强化XAI方法的作用。此外，我们提出一种利用解释进行根因分析的实用方法，应用于风力发电机性能监测场景，这将有助于减少停机时间并提升现场风机的利用率。