This study proposes a coil current model and an energy storage motor current (ESMC) model of circuit breakers (CBs) with spring operated mechanism. To make sure the signals generated by the models are identical to the...
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This study proposes a coil current model and an energy storage motor current (ESMC) model of circuit breakers (CBs) with spring operated mechanism. To make sure the signals generated by the models are identical to the actual ones, this study proposes a stochastic optimisation algorithm to optimise the model parameters. Based on the data produced by the optimised models, two fault diagnosis methods are proposed to assess operational condition and detect faults. The first method is based on fast template matching, which adopts K-means clustering algorithm to cluster the data and form a template library. The second one combines deep belief network and Softmax classifier, which can not only extract high level information of the characteristic signals, but also avoid the negative impact of the large dimension on classification results. In the simulation studies, the two methods are tested on various scenarios and their merits are demonstrated, respectively, where the latter one shows superior performance.
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