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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 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.
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Jason Poon
Palak Jain
Ioannis C. Konstantakopoulos
IEEE Transactions on Power Electronics
University of California, Berkeley
National University of Singapore
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Poon et al. (Fri,) studied this question.
www.synapsesocial.com/papers/6a04c41219daca77e62d31cf — DOI: https://doi.org/10.1109/tpel.2016.2541342
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