A Novel Soft Fault Detection and Diagnosis Method for a DC/DC Buck Converter Based on Contrastive Learning

DC/DC converter is widely used in power electronic applications as a critical part of power supply. During operation, component parameters of the dc/dc converter gradually degrade subjected to various environmental stresses. When they degrade to deviate from the normal value, soft faults will occur and directly affect the functionality and reliability of the whole system. To identify these anomalies effectively, a novel soft fault detection and diagnosis method based on contrastive learning is proposed for a dc/dc buck converter. First, the output voltage ripples of the converter under healthy and faulty conditions are collected. Then, a feature representation of the voltage ripple is obtained through hierarchical contrastive learning at temporal-level and instance-level. Based on the learned representation, a support vector machine classifier is established to detect and locate soft faults. The proposed method does not need extra hardware or signal injection, thereby reducing the cost and complexity. Simulation and physical experiment results verify the effectiveness and robustness of the proposed method.