Abstract
High-voltage circuit breakers (HVCBs) play an essential role in electrical power systems, which can ensure and control the smooth operation of power grids. Therefore, a fault diagnosis method of HVCBs based on Kernel Principal Component Analysis (KPCA) is proposed in this paper. As the fault data of HVCBs have the characteristic of multi-dimensional nonlinearity, the proposed method calculates inner kernel function of HVCBs' closing current in the original data space so as to achieve nonlinear mapping to the feature space of the original data. Afterwards, feature extraction and pattern classification of fault data can be accomplished in the feature space by means of monitoring SPE statistics. Experiment results have proved that the KPCA method can effectively improve the precision of fault diagnosis of HVCBs.
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