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Nonnegative Matrix Factorization Aided Principal Component Analysis for High-Resolution Partial Discharge Image Compression in Transformers
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Abstract
In this study, the development of nonnegative matrix factorization aided principal component analysis (NMF-PCA) algorithm is proposed to solve the problem that the covariance matrix cannot be computed due to the extremely high vector space caused by the “matrix-to-vector” transformation when principal component analysis (PCA) is applied to high-resolution image compression, which is further employed for PD gray image compression and recognition. In the proposed NMF-PCA algorithm, nonnegative matrix factorization (NMF) is firstly employed to decompose the high-resolution image into base matrices W and coefficient matrices H with lower dimension. Then, PCA is adopted to extract several principal components from the vectors of W and H as features. A fuzzy C-means (FCM) clustering method is responsible for PD classification and features evaluation. Using a traditional pulse current detector for PD experiment, 177 gray images associated with four PD defect types are obtained for NMF-PCA testing. The results of algorithm performance evaluation show that NMF and PCA both have fast responding time when r is less 3, which is suitable for PD on-line analysis and diagnosis. The recognition results of experimental PD samples demonstrate that only is the feature set FH extracted from the coefficient matrix H fit for PCA compression of PD gray images. Meanwhile, the maximum successful clustering rate 0.9661 is achieved by 3D features of FH with r = 2, which is much higher than 0.8023 of traditional PRPD operators. In addition, the FCM validity measures report that the features obtained by NMF-PCA have better aggregation characteristic than PRPD statistical operators. The obtained results demonstrate that the proposed NMF-PCA algorithm could provide an effective tool for PD diagnosis, and it is easy to extend to other image or matrix applications
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References
M.H. Wang, Partial Discharge Pattern Recognition of Current Transformers Using an ENN, IEEE Transactions on Power Delivery, vol. 20 n. 3, July 2005, pp. 1984 – 1990.
C.S. Kim, T. Kondo, T. Mizutani, Change in PD Pattern with Aging, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 11 n. 3, February 2004, pp. 13 – 18.
C. Mazzetti, F.M.F. Mascioli, M. Baldini, M. Panella, R. Risica, R. Bartnikas, Partial Discharge Pattern Recognition by Neuro-Fuzzy Networks in Heat-Shrinkable Joints and Terminations of XLPE Insulated Distribution Cables, IEEE Transactions on Power Delivery, vol. 13 n. 4, July 2006, pp. 1035 – 1044.
A. Cavallini, G.C. Montanari, F. Puletti, A. Contin, A New Methodology for the Identification of PD in Electrical Apparatus: Properties and Applications, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 12 n. 2, April 2005, pp. 203 – 215.
J. Li, R.J. Liao, S. Grzybowski, L.J. Yang, Oil-paper Aging Evaluation by Fuzzy Clustering and Factor Analysis to Statistical Parameters of Partial Discharges, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 17 n.3, June 2010, pp. 756 – 763.
A. Contin, A. Cavallini, G.C. Montanari, G. Pasini, F. Puletti, Digital Detection and Fuzzy Classification of Partial Discharge Signals, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 9 n. 3, June 2002, pp. 335 – 348.
E.M. Lalitha, L. Satish, Wavelet Analysis for Classification of Multi-Source PD Patterns, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 7 n. 1, February 2000, pp. 40 – 47.
F.C. Gu, H.C. Chang, F.H. Chen, C.C. Kuo, C.H. Hsu, Application of the Hilbert-Huang Transform with Fractal Feature Enhancement on Partial Discharge Recognition of Power Cable Joints, IET Science Measurement & Technology, vol. 6 n. 6, November 2012, pp. 440 – 448.
H. Okubo, N. Hayakawa, A Novel Technique for Partial Discharge and Breakdown Investigation Based on Current Pulse Waveform Analysis, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 12 n. 4, August 2005, pp. 736 – 744.
Chang, W.-Y., Application of fuzzy C-means clustering approach and genetic algorithm to partial discharge pattern recognition, (2012) International Review of Electrical Engineering (IREE), 7 (4), pp. 5213-5220.
Tang, J., Tao, J., Zhang, X., Liu, F., Multiple SVM-RFE for feature subset selection in partial discharge pattern recognition, (2012) International Review of Electrical Engineering (IREE), 7 (4), pp. 5240-5246.
Tang, J., Zhou, J., Zhang, X., Tao, J., Feature extraction method based on pulse-coupled neural networks for ultra high frequency partial discharge in transformers, (2012) International Review of Electrical Engineering (IREE), 7 (1), pp. 3270-3279.
A. Krivda, E. Gulski, L. Satish, W.S. Zaengl, The Use of Fractal Features for Recognition of 3-D Discharge Patterns, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 2 n. 5, October 1995, pp. 889 – 892.
L. Satish, W.S. Zaengl, Can Fractal Features be Used for Recognizing 3-D Partial Discharge Patterns? IEEE Transactions on Dielectrics and Electrical Insulation, vol. 2 n. 3, June 1995, pp. 352 – 359.
J. Li, C.X. Sun, S. Grzybowski, Partial Discharge Image Recognition Influenced by Fractal Image Compression, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 19 n. 2, April 2012, pp.543 – 549.
Liao, R., Wang, K., Yang, L., Yuan, L., Li, J., Partial discharge signal classification utilizing time-frequency representation and two directional 2DPCA, (2012) International Review of Electrical Engineering (IREE), 7 (1), pp. 3358-3369.
R.J. Liao, K. Wang, L.J. Yang, L. Duan, J. Li, A New Hybrid Feature Extraction Method for Partial Discharge Signals Classification, Pryzglad Elektrotechniczny, vol. 88 n. 11A, November 2012, pp. 191 – 195.
Liao, R., Wang, K., Yang, L., Li, J., Nie, S., Yuan, L., Binary particle swarm optimization selected time-frequency features for partial discharge signal classification, (2012) International Review of Electrical Engineering (IREE), 7 (5), pp. 5905-591.
K. Polat, S. Gunes, Automatic Determination of Diseases Related to Lymph System from Lymphography Data Using Principles Component Analysis (PCA), Fuzzy Weighting Pre-processing and ANFIS, Expert System with Applications, vol. 33 n. 3, October 2007, pp. 636 – 641.
H.T. Zhao, P.C. Yuen, J.T. Kwok, A Novel Incremental Principal Component Analysis and Its Application for Face Recognition, IEEE Transactions on Systems Man and Cybernetics Part B - Cybernetics, vol. 36 n. 4, August 2006, pp. 873 – 886.
E.P. de Moura, C.R. Souto, A.A. Silva, M.A.S. Irmao, Evaluation of Principal Component Analysis and Neural Network Performance for Bearing Fault Diagnosis from Vibration Signal Processing by RS and DF Analysis, Mechanical Systems and Signal Processing, vol. 25 n. 5, July 2011, pp. 1765 – 1772.
M. Pagani, D. Salmaso, G. Rodriguez, D. Nardo, F. Nobili, Principal Component Analysis in Mild and Moderate Alzheimer's Disease - A Novel Approach to Clinical Diagnosis, Psychiatry Research-Neuroimaging, vol. 173 n. 1, July 2009, pp. 8 – 14.
V.F. Pires, T.G. Amaral, J.F. Martins, Power Quality Disturbances Classification Using the 3-D Space Representation and PCA based Neuro-Fuzzy Approach, Expert Systems with Applications, vol. 38 n. 9, September 2009, pp. 11911 – 11917.
J. Yang, D. Zhang, A.F. Frangi, J.Y. Yang, Two-dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26 n. 1, January 2004, pp. 131 – 137.
D.Q. Zhang, Z.H. Zhou, (2D)2PCA: Two-directional Two-dimensional PCA for Efficient Face Representation and Recognition, Neurocomputing, vol. 69 n. 1-3, December 2005, pp. 224 – 231.
P. Nagabhushan, D.S. Guru, B.H. Shekar, (2D)2FLD: An Efficient Approach for Appearance based Object Recognition, Neurocomputing, vol. 69 n. 7-9, March 2006, pp. 934 – 940.
K.Y. Yeung, W.L. Ruzzo, Principal Component Analysis for Clustering Gene Expression Data, Bioinformatics, vol. 17 n. 9, September 2001, pp. 763 – 774.
J. Li, C.X. Sun, S. Grzybowski, C.D. Taylor, Partial Discharge Image Recognition Using a New Group of Features, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 13 n. 6, December 2006, pp. 1245 – 1253.
H.C. Chen, F.C. Gu, M.H. Wang, A Novel Extension Neural Network Based Partial Discharge Pattern Recognition Method for High-voltage Power Apparatus, Expert Systems with Applications, vol. 39 n. 3, February 2012, pp. 3423 – 3431.
A.Y. Liu, Y. Zhang, E. Gehan, R. Clarke, Block Principal Component Analysis with Application to Gene Microarray Data Classification, Statistics in Medicine, vol. 21 n. 22, November 2002, pp. 3465 – 3474.
D.D. Lee, H.S. Seung, Learning the Parts of Objects by Non-negative Matrix Factorization, Nature, vol. 401 n. 6755, October 1999, pp. 788 – 791.
C.J. Lin, Projected Gradient Methods for Nonnegative Matrix Factorization, Neural Computation. vol. 19 n. 10, October 2007, pp. 2755 – 2779.
A. Hirata, S. Nakata, Z.I. Kawasaki, Toward Automatic Classification of Partial Discharge Sources with Neural Networks, IEEE Transactions on Power Delivery, vol. 21 n. 1, January 2006, pp. 526 – 527.
L. Hao, P.L. Lewin, Partial Discharge Source Discrimination using a Support Vector Machine, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 17 n. 1, February 2010, pp. 189 – 197.
M.M.A. Salama, R. Bartnikas, Fuzzy Logic Applied to PD Pattern Classification, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 7 n. 1, February 2000, pp. 118 – 123.
T.K. Abdel-Galil, Y.G. Hegazy, M.M.A. Salama, R. Bartnikas, Partial Discharge Pulse Pattern Recognition using Hidden Markov Models. IEEE Transactions on Dielectrics and Electrical Insulation, vol. 11 n. 4, August 2004, pp. 715 – 723.
J.C. Bezdek, R. Ehrlich, W. Full, FCM – The Fuzzy C-Means Clustering-Algorithm, Computers & Geosciences, vol. 10 n. 2-3, 1984, pp. 191 – 203.
D. Dembele, P. Kastner, Fuzzy C-Means Method for Clustering Microarry Data. Bioinformatics, vol. 19 n. 8, May 2003, pp. 973 – 980.
A. Cavallini, G.C. Montanari, F. Ciani, Analysis of Partial Discharge Phenomenon in Paper-Oil Insulation Systems as a Basis for Risk Assessment Evaluation, Processing of the 2005 IEEE International Conference on DIELECTRIC LIQUIDS, ~ICDL 2005~, June 26 – July 1, 2005, pp. 241-244, Coimbra, Portugal.
T.Y. Jiang, J. Li, Y.B. Zheng, C.X. Sun, Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges. Energies, vol. 4 n. 7, July 2011, pp. 1087 – 1101.
IEC 60270: 2000 Standard, High-voltage Test Techniques – Partial Discharge Measurements, 3rd Edition, 2000.
R.J. Liao, S.W. Liang, C.X. Sun, L.J. Yang, H.G. Sun, A Comparative Study on Thermal Aging of Transformer Insulation Paper Impregnated in Natural Ester and in Mineral Oil, European Transactions on Electrical Power, vol. 20 n. 4, May 2010, pp. 518 – 533.
R.J. Liao, C. Tang, L.J. Yang, S. Grzybowski, Thermal Aging Micro-scale Analysis of Power Transformer Pressboard, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 15 n.5, October 2008, pp. 1281 – 1287.
R.J. Liao, L.J. Yang, J. Li, S. Grzybowski, Aging Condition Assessment of Transformer Oil-paper Insulation Model based on Partial Discharge Analysis, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 18 n.1, February 2011, pp. 303 – 311.
R. Candela, G. Mirelli, R. Schifani, PD Recognition By Means of Statistical and Fractal Parameters and a Neural Network, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 7 n.1, February 2000, pp. 87 – 94.
E. Gulski, A. Krivda, Neural Networks as a Tool for Recognition of Partial Discharges, IEEE Transactions on Electrical Insulation, vol. 28 n.6, December 1993, pp. 984 – 1001.
R.E. James, B.T. Phung, Development of Computer-Based Measurements and Their Application to PD Pattern-Analysis, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 2 n. 5, October 1995, pp. 87 – 94.
R.J. Liao, K. Wang, L.J. Yang, T.C. Zhou, S.X. Zheng, Study on Thermal Aging Condition Assessment of Oil-Paper Insulation Based on Statistical Features of Partial Discharge, Processing of the 2005 IEEE International Conference on PROPERTIES AND APPLICATIIONS OF DIELECTRIC MATERIALS, ~ICPADM 2009~, July 19 – 23, 2009, pp. 325 – 328, Harbin, China.
G.A. Bakken, P.C. Jurs, Classification of Multidrug Reversal Agents Using Structure-based Descriptors and Linear Discriminant Analysis, Journal of Medicinal Chemistry, vol. 43 n. 23, November 2000, pp. 4534 – 4541.
J.B. Kruskal, Multidimensional-scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis, Pyschometrika, vol. 29 n. 1, 1964, pp. 1 – 27.
J.B. Tenebaum, Mapping a Manifold of Perceptual Observations, Advances in Neural Information Processing Systems 10, vol. 10, 1998, pp. 682 – 688.
S.T. Roweis, L.K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol. 290 n. 5500, December 2000, pp. 2323 – 2326.
M. Belkin, P. Niyogi, Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering, Advances in Neural Information Processing Systems 14, Vols 1 And 2, vol. 14, 2002, pp. 585 – 591.
A.A. Mazroua, R. Bartnikas, M.M.A. Salama, Discrimination Between PD Pulse Shapes Using Different Neural-Network Paradigms, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 1 n. 6, December 1994, pp. 1119 – 1131.
J. Li, T.Y. Jiang, R.F. Harrison, S. Grzybowski, Recognition of Ultra High Frequency Partial Discharge Signals Using Multi-scale Features, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 19 n. 4, August 2012, pp. 1412 – 1420.
P. Paatero, U. Tapper, Positive Matrix Factorization – A Nonnegative Factor Model with Optimal Utilization of Error-Estimates of Data Values, Environmetrics, vol. 5 n. 2, June 1994, pp. 111 – 126.
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