We provide a condition monitoring system for wind farms, based on normal behaviour modelling using a probabilistic multi-layer perceptron with transfer learning via fine-tuning. The model predicts the output power of the wind turbine under normal behaviour based on features retrieved from supervisory control and data acquisition (SCADA) systems. Its advantages are that (i) it can be trained with SCADA data of at least a few years, (ii) it can incorporate all SCADA data of all wind turbines in a wind farm as features, (iii) it assumes that the output power follows a normal density with heteroscedastic variance and (iv) it can predict the output of one wind turbine by borrowing strength from the data of all other wind turbines in a farm. Probabilistic guidelines for condition monitoring are given via a CUSUM control chart. We illustrate the performance of our model in a real SCADA data example which provides evidence that it outperforms other probabilistic prediction models.

翻译：我们提出了一种基于正常行为建模的风电场状态监测系统，该系统采用概率型多层感知机，并通过微调实现迁移学习。模型根据从监控与数据采集系统（SCADA）中提取的特征，预测风力发电机在正常行为下的输出功率。其优势在于：(i) 可利用至少数年时长的SCADA数据训练模型，(ii) 能将风电场所有风力发电机的SCADA数据作为特征纳入模型，(iii) 假设输出功率服从具有异方差的正态分布，(iv) 通过借用力场中其他风力发电机数据的信息，预测单台风力发电机的输出。通过CUSUM控制图给出了状态监测的概率性指导准则。我们基于实际SCADA数据示例验证了模型性能，结果表明该模型优于其他概率预测模型。