Rotating Rectifier Fault Diagnosis of Nuclear Multiphase Brushless Excitation System Based on DTW Metric and kNN Classifier

A multiphase annular brushless excitation system is widely used in large-capacity nuclear power units. Accurate fault diagnosis of the rotating rectifier is of great significance to improve the reliability of the excitation system. However, the traditional diagnosis method based on the harmonic analysis of stator field current has some deficiencies. In this article, a novel diagnosis method using field current waveforms and artificial intelligence is presented. First, the various shape features in field current waveforms of the different rotating rectifier faults are analyzed. Then, the field current waveforms are used as the input of a hybrid algorithm based on the dynamic time warping (DTW) metric and the k-nearest neighbors (kNN) classifier (DTW-kNN). That is, a DTW metric is used to calculate the distance among the shape features in field current waveforms and kNN classifier is used to diagnose the specific rotating rectifier fault. Finally, experiments on an 11-phase prototype prove the effectiveness of the hybrid method DTW-kNN. It is worth mentioning that an improved training set, including all trends of field current waveforms, should be selected to avoid the asymmetry between each pair of field poles. The learning method provides a new idea for fault diagnosis of the rotating rectifier.