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, data-driven machine learning methods
have outperformed parametric, physics-informed approaches. However, they are
often criticised for being opaque "black boxes" which raises concerns regarding
their robustness in non-stationary environments, such as 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 SCADA data. It combines domain-specific
considerations with Shapley Values and the latest findings from XAI for
regression. Our results suggest, that learned strategies can be better
indicators for model robustness than validation or test set errors. Moreover,
we observe that highly complex, state-of-the-art ML models are prone to learn
physically implausible strategies. Consequently, we compare several measures to
ensure physically reasonable model behaviour. Lastly, we propose the
utilization of XAI in the context of wind turbine performance monitoring, by
disentangling environmental and technical effects that cause deviations from an
expected turbine output. We hope, our work can guide domain experts towards
training and selecting more transparent and robust data-driven wind turbine
power curve models.

该论文引入了可解释的人工智能框架，通过 Shapley 值和最新的可解释 AI for 回归方法，从 SCADA 数据中学习风力发电机功率曲线模型。研究结果表明，学习策略可以更好地指示模型的鲁棒性，比验证集或测试集错误更有用。本文提出了在风力涡轮机性能监测中利用 XAI 的方案，最终目标是选取更透明和鲁棒的数据驱动风力涡轮机功率曲线模型。