Investigation of deep transfer learning for cross-turbine diagnosis of wind turbine faults

Abstract

Data-driven fault diagnosis of wind turbines has gained popularity, and various deep learning models have been developed accordingly with massive amounts of data and achieved an excellent diagnosis performance. However, most existing deep learning models require a similar distribution of both training and testing data, thus the trained model cannot generalize new wind turbines with different data distributions. In addition, there are insufficient fault data in practice, and therefore the cost of training a new model from scratch is extremely high. To solve these problems, a cross-turbine fault diagnosis method based on deep transfer learning is proposed for wind turbines with the available supervisory control and data acquisition (SCADA) data. To better capture the spatial features of SCADA data, a deep multi-scale residual attention convolutional neural network (DMRACNN) is first designed. Then, the distribution differences between the source and target domain data are aligned at feature level. Specifically, we investigate the transfer performance of four different domain adaptation metrics. We evaluate our proposed method using SCADA data from two wind turbines to compare the diagnostic performance of four basic networks combined with four transfer metrics. Compared with traditional deep learning methods, our proposed DMRACNN achieved significant performance improvements. A cross-validation experiment using two turbines demonstrates the strong generalization ability of the proposed method.