Research and Application of Capacitor Fault Prediction forLocomotive Traction Converter - ZHAN Yanhao; YANG Jiawei; LU Qingsong (Kongzhi Yu Xinxi Jishu)
In order to avoid the increase of input power load caused by the performance degradation of locomotive traction converter capacitor that affects the safe and reliable operation of a traction system, a capacitor fault prediction method is proposed in this paper. Capacitor parameters are identified by detecting output voltage ripples of capacitor, and capacitor parameters are fitted based on LS-SVM algorithm to identify the degradation characteristics of capacitor, so as to realize fault prediction of capacitor. Using this method and BP neural network prediction method, taking ESR as capacitance eigenvalue as an example, fault prediction of resonant capacitor and support capacitor in the middle DC circuit of traction converter is carried out. The results show that LS-SVM model has small error, high precision and can better reflect the actual changes. The LS-SVM model is used to pre-warning and verify the on-site operation states and fault situations of the capacitors in recent two years. The results show that the accuracy of this method is higher than 90%, which verifies the effectiveness of the proposed method for capacitor fault prediction.