Partial discharge pattern recognition using variable predictive model‐based class discrimination with kernel partial least squares regression. Issue 3 (1st May 2018)

• Record Type: Journal Article
• Authors: Jia, Yafei; Zhu, Yongli
• Is Part Of: IET science, measurement & technology. Volume 12:Issue 3(2018)
• Page Start: 360
• Page End: 367
• Publication Date: 2018-05-01
• Subjects: partial discharge measurement -- pattern recognition -- prediction theory -- least squares approximations -- regression analysis -- power transformer insulation -- feature extraction; partial discharge pattern recognition method -- variable predictive model‐based class discrimination -- PD pattern recognition method -- insulation condition evaluation -- power transformer -- feature extraction -- LS regression -- improved VPMCD -- kernel partial least square regression -- KPLS regression -- UCI dataset -- intelligent patter recognition method -- backpropagation neural network -- support vector machine -- supervised learning algorithm; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2017.0345
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : The pattern recognition of partial discharge (PD) is critical to evaluate the insulation condition and locate the defect of a power transformer. The existing pattern recognition methods fail to make use of the inter‐relations of the extracted features of PD signals. In fact, the inter‐relations can show distinct dissimilarities between different classes of the signals. To overcome the defect of existing pattern recognition methods, the variable predictive model‐based class discrimination (VPMCD), a new pattern recognition method, is introduced for the pattern recognition of PD in this study. However, the original VPMCD lacks general expression ability of the inter‐relations of extracted features and inherits the shortcomings of least squares (LS) regression. To overcome the above defects, an improved VPMCD based on kernel partial LS (KPLS) regression, which is called as KPLS‐VPMCD, is proposed in this study. Experiments and analyses are implemented using both UCI datasets and the extracted features of PD signals. The experiments show that the performance of the proposed KPLS‐VPMCD is better than those of the existing methods such as VPMCD, back propagation neural networks, and support vector machines. The conclusion is that KPLS‐VPMCD is an efficient supervised learning algorithm with consistency and good performance for PD pattern recognition.