A new class of fault recognition method for wind turbine systems based on deep learning

Conventional fault recognition algorithms can only recognize the classes of faults that have appeared in wind turbine systems. However, if a new category of faults appears, the traditional algorithm can only misclassify it into the class of pre-existing faults. In this paper, a new class fault recognition method based on deep learning is proposed. Firstly, the initialized model is built using known fault data, including detectors and classifiers. Secondly, the new class fault data detected by the detectors are put into the cache, and when the cache overflows, the new class faults are augmented with data using generative adversarial networks. Finally, the new class fault data and the generated data are added to the initial training set, and the structure and weights of the initialized model are updated using the new training set. In this way, the purpose of recognizing the new class of faults is achieved. The experimental results show that the proposed detection algorithm can effectively detect new class faults and the model can efficiently solve the problem of the new class of fault recognition after data augmentation.