Kernel Entropy-Based Classification Approach for Superbuck Converter Circuit Fault Diagnosis

How to extract the fault feature and how to design the classification algorithm are the two most critical problems in power electronic circuits (PECs) fault diagnosis. Based on a kernel entropy component analysis theory, combining an extreme learning machine classification algorithm, this paper explores the feasibility of applying an ensemble approach, called KECA-ELM, to deal with the hard fault and soft fault diagnosis in a superbuck converter circuit (SCC). This approach can reduce the influence of the complex correlation between the data on the accuracy of fault classification. Furthermore, it can compress the feature dimension of the data while maintaining the feature discriminating power and reduce the computation in the classification stage. We record the signal from the output of the circuit, analyze their static and dynamic electrical performance, and then select the representative feature parameters. These feature parameters are combined into feature vectors which can reflect the health status of the PECs. Finally, simulation and physical experiment are presented in the SCC to demonstrate the fault classification ability of the proposed approach.