Parameter Degradation Features Selection Based on Multiple Evaluation Index in a DC-DC Converter

DC-DC converters play an important role in electrical systems. Its fault state has a major impact on the operation of the back-end components and the entire electrical system. Parametric degeneration diagnosis is essential which can decrease the likelihood of serious faults occurring, enormous economic loss, and severe injury. To effectively and rapidly diagnose the parametric fault in DC-DC converters, a sensitive fault features extraction and selection method is proposed. Firstly, statistical features in the time domain and wavelet energy and entropy values in the time-frequency domain of the output voltage and the input current were collected for each type of fault. Then, the multiple evaluation indexes method is used to select the most sensitive fault features. Finally, simulation results validate the practicability and effectiveness of the proposed method.