Wind power plays a key role in reducing global carbon emission. The power curve provided by wind turbine manufacturers offers an effective way of presenting the global performance of wind turbines. However, due to the complicated dynamics nature of offshore wind turbines, and the harsh environment in which they are operating, wind power forecasting is challenging, but at the same time vital to enable condition monitoring (CM). Wind turbine power prediction, using supervisory control and data acquisition (SCADA) data, may not lead to the optimum control strategy as sensors may generate non-calibrated data due to degradation. To mitigate the adverse effects of outliers from SCADA data on wind power forecasting, this paper proposed a novel approach to perform power prediction using high-frequency SCADA data, based on isolate forest (IF) and deep learning neural networks. In the predictive model, wind speed, nacelle orientation, yaw error, blade pitch angle, and ambient temperature were considered as input features, while wind power is evaluated as the output feature. The deep learning model has been trained, tested, and validated against SCADA measurements. Compared against the conventional predictive model used for outlier detection, i.e. based on Gaussian Process (GP), the proposed integrated approach, which coupled IF and deep learning, is expected to be a more efficient tool for anomaly detection in wind power prediction.

中文翻译：

---

基于高频SCADA数据结合隔离森林和深度学习神经网络的风电功率预测

风能在减少全球碳排放方面发挥着关键作用。风力涡轮机制造商提供的功率曲线提供了一种有效的方式来呈现风力涡轮机的全球性能。然而，由于海上风力涡轮机的复杂动力学特性以及它们运行的​​恶劣环境，风电预测具有挑战性，但同时对于实现状态监测 (CM) 至关重要。使用监控和数据采集 (SCADA) 数据的风力涡轮机功率预测可能不会导致最佳控制策略，因为传感器可能会因性能下降而生成未校准的数据。为了减轻 SCADA 数据异常值对风电功率预测的不利影响，本文提出了一种使用高频 SCADA 数据进行功率预测的新方法，基于隔离森林（IF）和深度学习神经网络。在预测模型中，风速、机舱方向、偏航误差、叶片桨距角和环境温度被视为输入特征，而风力被评估为输出特征。深度学习模型已经根据 SCADA 测量进行了训练、测试和验证。与用于异常值检测的传统预测模型相比，即基于高斯过程（GP），所提出的结合 IF 和深度学习的集成方法有望成为风电预测中更有效的异常检测工具。深度学习模型已经根据 SCADA 测量进行了训练、测试和验证。与用于异常值检测的传统预测模型相比，即基于高斯过程 (GP)，所提出的结合 IF 和深度学习的集成方法有望成为风电预测中更有效的异常检测工具。深度学习模型已经根据 SCADA 测量进行了训练、测试和验证。与用于异常值检测的传统预测模型相比，即基于高斯过程 (GP)，所提出的结合 IF 和深度学习的集成方法有望成为风电预测中更有效的异常检测工具。