Fault detection for power electronic converters based on continuous wavelet transform and convolution neural network

Abstract

With the rapid development of new energy vehicles, the reliability and safety of Brushless DC motor drive system, the core component of new energy vehicles, has been widely concerned. The traditional open circuit fault detection method of power electronic converters have the problem of poor feature extraction ability because of inadequate signal processing means, which lead to low recognition accuracy. Therefore, a fault recognition method based on continuous wavelet transform and convolutional neural network (CWT-CNN) is proposed. It can not only adaptively extract features, but also avoid the complexity and uncertainty of artificial feature extraction. The three-phase current signal is converted into time-frequency spectrum by continuous wavelet transform as the input data of AlexNet. At the same time, the changes of time domain and frequency domain under different fault modes are analyzed. Finally, the softmax classifier with Adam optimizer is used to classify the fault features extracted by CNN to realize the state recognition of different fault modes of power electronic converter. The experimental results show that the CWT-CNN model achieves satisfactory fault detection accuracy under different working conditions and different fault modes. The effectiveness and superiority of the proposed method are verified by comparing with other networks.

Fault detection and isolation in system of multiple sources of energy using hierarchical Bayesian belief networks

“…Analyzing the wavelet transform of power converter faults through effective machine learning techniques based on K nearest neighbors is discussed in [17]. Similarly, Sun et al [18]…”

A Federated Learning-Based Fault Detection Algorithm for Power Terminals

“…Visible images, infrared images, and ultraviolet images of power equipment were fused to train a deep learning-based fault detection model [6] in a power system. Improved random forest was used to detect the power outage accident of the power terminal in reference [7], and continuous wavelet transform and convolution neural network were adopted to detect faults for power electronic converters in reference [8].…”