Power Cable Fault Feature Extraction Based on Wavelet and Segmentation
To extract the features of the online power cable fault information for the fault recognition,this paper presents a new method which combines the wavelet transform and segmentation. In the method,the power cable fault pattern would be broken into segments,and then those segments are processed by wavelet packet decomposition. After that,the weighted sum of decomposed wavelet coefficients is calculated by using the logarithm values of the summations as the new fault features. At last,Fisher criterion is used to measure the performance of the new features compared with the original features. The Matlab simulation experiments show that the new features have the better performance which is evident from the Fisher values. The within-class distances of the single-phase faults have the smaller Fisher values which are in favor of the latter classifier design and the fault recognition accuracy.
feature extraction segmentation wavelet Fisher criteria power cable
Wang Mei Xu Jian Wu Xiaowei
Xian University of Science and Technology,710054,China
国际会议
西安
英文
2341-2345
2011-12-23（万方平台首次上网日期，不代表论文的发表时间）