A novel method for differentiating and clustering multiple partial discharge sources using S transform and bag of words feature

Transformers are known as one of the most important equipment in power system transmission and distribution networks. Partial Discharge (PD) measurement and PRPD interpretation is a powerful tool to monitor the situation and evaluating the risk of power transformer failure. Multiple discharge sources affect the accuracy of interpretation of PRPD. This paper presents a PD signal separation algorithm based on extraction of high level image features. The time-frequency S transform (ST) is applied to the PD signal waveforms, acquired by digital detection instruments at 100 MS/s. The resultant ST matrix is then converted to gray scale image from which high level features are extracted using Bag of Words (BoW). Principle component analysis (PCA) transform is applied to BoW feature to reduce the dimension of features. Calinski-Harabasz criterion is calculated to identify the number of active sources and then Gaussian mixture model (GMM) clustering is used to discover clusters in the feature space. The proposed separation algorithm is examined with mixed current impulse signals acquired from PD experiments on artificial multi-defect models. The separation results indicate that the proposed algorithm is effective for identifying the number of active sources and separating mixed PD signals initiated from multiple sources.

[1]  G. Robles,et al.  Partial discharge and noise separation by means of spectral-power clustering techniques , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[2]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[3]  W. Marsden.  I and J , 2012 .

[4]  A. Cavallini,et al.  Analysis of partial discharge phenomena in paper-oil insulation systems as a basis for risk assessment evaluation , 2005, IEEE International Conference on Dielectric Liquids, 2005. ICDL 2005..

[5]  Jian Li,et al.  A hybrid algorithm based on s transform and affinity propagation clustering for separation of two simultaneously artificial partial discharge sources , 2015, IEEE Transactions on Dielectrics and Electrical Insulation.

[6]  Asghar Akbari,et al.  A method for discriminating original pulses in online partial discharge measurement , 2011 .

[7]  T. Moon.  The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[8]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[9]  Mehmed Kantardzic,et al.  Data-Mining Concepts , 2011 .

[10]  A. Contin,et al.  Classification and separation of partial discharge signals by means of their auto-correlation function evaluation , 2009, IEEE Transactions on Dielectrics and Electrical Insulation.

[11]  Alfredo Contin,et al.  Use of a mixed-Weibull distribution for the identification of PD phenomena [rotating machines] , 1995 .

[12]  W. Koltunowicz,et al.  Synchronous multi-channel PD measurements , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[13]  Alfredo Contin,et al.  Advanced PD inference in on-field measurements. II. Identification of defects in solid insulation systems , 2003 .

[14]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[15]  Robert Marti,et al.  Which is the best way to organize/classify images by content? , 2007, Image Vis. Comput..

[16]  V. P. Darabad,et al.  An efficient diagnosis method for data mining on single PD pulses of transformer insulation defect models , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[17]  Alfredo Contin,et al.  Advanced PD inference in on-field measurements. I. Noise rejection , 2003 .

[18]  E. M. Lalitha,et al.  Wavelet analysis for classification of multi-source PD patterns , 2000 .

[19]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[20]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[21]  J. A. Hunter,et al.  Discrimination of multiple PD sources using wavelet decomposition and principal component analysis , 2011, IEEE Transactions on Dielectrics and Electrical Insulation.

[22]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[23]  G. Montanari,et al.  Digital detection and fuzzy classification of partial discharge signals , 2002 .

[24]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Wei Li,et al.  Blind Source Separation of Mixed PD Signals Produced by Multiple Insulation Defects in GIS , 2010, IEEE Transactions on Power Delivery.

[26]  Massimo Pompili,et al.  Phase relationship of PD pulses in dielectric liquids under ac conditions , 2000 .