In order to realize the unsupervised extraction and identification of fault features in power electronic circuits, we proposed a fault\ndiagnosis method based on sparse autoencoder (SAE) and broad learning system (BLS). Firstly, the feature is extracted by the\nsparse autoencoder, and the fault samples and feature vectors are combined as the input of the broad learning system. The broad\nlearning system is trained based on the error precision step update method, and the system is used to the fault type identification.\nThe simulation results of the thyristor fault diagnosis of the three-phase bridge rectifier circuit show that the method is effective\nand has better performance than other traditional methods.
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