基于时频模态特征提取的牵引网行波故障定位方法研究OA

To address the challenge of identifying traveling wave components,including fault point incident waves,reflected waves,and waves reflected by auto-transformer(AT),which complicates fault location in all-parallel AT traction networks,this paper proposes a wave similarity-based fault location method using adaptive projection intrinsically transformed multivariate empirical mode decomposition(APIT-MEMD).The APIT-MEMD algorithm is applied to adaptively decompose fault signals from multi-conductor lines in both directions to extract transient high-frequency characteristics representing different components of fault traveling waves.By constructing the cross-correlation coefficient matrix of different wave mode components to identify traveling waves along different paths and calculating the corresponding maximum time delay,fault location in the traction net-work is achieved.Experimental results demonstrate that the proposed method,based on time-frequency mode feature extraction,achieves a fault location error within 102 m with an average absolute error of 49 m.Compared with the results of the multivariate empirical mode decomposition(MEMD)algorithm using different projection parameters,the proposed method effectively improves fault location accuracy.