基于MDS的电缆附件局部放电模式识别

Pattern Recognition for Partial Discharge of Cable Accessories Based on Multidim Ensional Scaling

针对电缆附件局部放电特征参数提取时,由于数据量不足难以形成图谱而导致特征提取困难这一问题,提出一种相位分割方法,同时结合多维尺度分析法(MDS)对特征值进行降维优化,识别出具有较高分类能力的最优特征量。通过在工频周期内对局放信号进行等角度分割,在每个分割区域进行特征值提取,获取更细微更具体的特征参数,再通过MDS对特征值降维优化以提高识别的速度和准确度。通过电力电缆附件典型缺陷的实验对比,结果表明该方法能在数据较少的情况下,较好地提取出特征值,且能得到更准确的识别结果。

A phase segmentation method is proposed to solve the problem that a small amount of data is difficult to form a map when extracting the characteristic parameters of partial discharges of cable accessories. At the same time, multidimensional scaling(MDS) is used to reduce the dimension of the eigenvalues and to extract the best features with higher classification ability. Through the division of partial discharge(PD) signals at equal angles in the power frequency cycle, the eigenvalue extraction is carried out in each of the divided regions to obtain more detailed and specific characteristic parameters, and then the feature values are optimized by MDS to improve the recognition speed and identify accuracy. The experimental results of the typical defects of various power cable accessories show that this method can extract better eigenvalues under fewer data and get better recognition results.