We present the analysis, design, and experimental implementation of a model-based fault detection and identification (FDI) method for switching power converters based on a linear-switched modeling approach. The proposed FDI approach is general in that it can be used to detect and identify arbitrary faults in components, sensors, and inputs in a broad class of switching power converters. More importantly, the modeling and implementation of the proposed FDI approach is flexible for both the converter topology and faults of interest; that is, one would require minimal effort to reconfigure an existing FDI implementation for a different converter topology or fault type. We show that the use of a linear-switched model, while introducing complexities in terms of modeling and real-time implementation, offer advantages over model-based FDI methods that rely on an averaged small signal model. Moreover, we show that the proposed FDI method can be integrated with the existing control system of the switching power converter, that is, no additional electrical or computation hardware is required. In essence, the FDI method enables a layer of intelligence on top of existing hardware protection such as fuses and circuit breakers.

In this thesis, we present experimental implementations and results for three different converter topologies, ranging from distributed AC grid-connected systems to distributed DC networked systems. The field-programmable gate array (FPGA) implementation enables fast fault detection and fault identification with speed on the order of application-specific implementations in literature, but with the advantage of being converter- and fault-agnostic in terms of modeling and implementation.




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