Real-time model-based fault diagnosis for switching power converters

We present the analysis, design, and experimental implementation of a fault diagnosis method for switching power converters using a model-based estimator approach. The fault diagnosis method enables efficient detection and identification of component and sensor faults, and is implemented on the same computation platform as the control system. The model-based estimator operates in parallel with the switching power converter, and generates an error residual vector that can be used to detect and identify particular component or sensor faults. This paper presents an experimental demonstration for a 1.2 kW rack-level uninterruptable power supply (UPS) dc-dc converter for data center applications. Simulation and experimental results demonstrate fault detection and identification for various component and sensor faults in the converter. Moreover, we show that the proposed fault diagnosis design and analysis methods are applicable to a broad class of converter topologies and fault types.