Wind Power Icing Fault Diagnosis Based on Slow Feature Analysis and Support Vector Machines

Blade icing is a worldwide problem in the field of wind power. A lot of prior knowledge is needed to detect blade icing by using mechanism modeling method, which cannot be satisfied in most cases. With the development of artificial intelligence (AI) technology, data-driven approaches have attracted widespread attention. In this paper, a combined strategy of slow feature analysis (SFA) and support vector machine (SVM) is proposed to detect the icing fault of wind blades. It is significant to improve the reliability and economy of wind turbines. First, SFA is used to extract the features that change slowly in the process. After that, the extracted slow features are input into SVM for fault detection. The lowest error rate of the proposed method is 0.234 when the number of show features is reduced to 5 while it is 0.317 by directly putting into SVM, which is verified by wind power experiments. So it is crucial to select the number of slow features.