A new approach for the diagnosis of different types of faults in dc–dc power converters based on inversion method

Abstract

This paper presents theory, a new approach and validation results for fault detection and isolation (FDI) in dc–dc power converters, based on inversion method. The developed method consists on the inversion-based estimation of faults and change detection mechanisms adapted to the power converters context. With the inverse model of a switched linear system, we have designed a real-time FDI algorithm with an integrated fuzzy logic scheme which detects and isolates abrupt changes (faults) at unknown time instants. A smoothing strategy is used to attenuate the effect of unknown disturbances and noise that are present at the outputs of this inverse model. Once the fault event is detected, a dedicated fuzzy-logic-based scheme is proposed to isolate the four types of faults: switch, voltage and current sensor, and capacitor. The performance of the proposed method is verified experimentally to detect and isolate the mentioned faults in the dc–dc boost power converter.