Model-Based Fault Detection and Identification for Switching Power Converters

We present the analysis, design, and experimental validation of a model-based fault detection and identification (FDI) method for switching power converters using a model-based state estimator approach. The proposed FDI approach is general in that it can be used to detect and identify arbitrary faults in components and sensors in a broad class of switching power converters. The FDI approach is experimentally demonstrated on a nanogrid prototype with a 380-V dc distribution bus. The nanogrid consists of four different switching power converters, including a buck converter, an interleaved boost converter, a single-phase rectifier, and a three-phase inverter. We construct a library of fault signatures for possible component and sensor faults in all four converters. The FDI algorithm successfully achieves fault detection in under 400 $\mu$s and fault identification in under 10 ms for faults in each converter. The proposed FDI approach enables a flexible and scalable solution for improving fault tolerance and awareness in power electronics systems.