and Electrical Insulation

IEEE TransacTIons on DIElEcTrIcs A PUBLICATION OF THE IEEE DIELECTRICS AND ELECTRICAL INSULATION SOCIETY

April 2015

Volume 22

and Electrical Insulation Number 2

ITDEIS

(ISSN 1070-9878)

FIFTIETH annIVErsarY oF IEEE TransacTIons on DIElEcTrIcs anD ElEcTrIcal InsUlaTIon

caBlEs Thermoplastic Cable Insulation Comprising a Blend of Isotactic Polypropylene and a Propylene-Ethylene Copolymer ...............................................................................................................C. D. Green, A. S. Vaughan, G. C. Stevens, A. Pye, S. J. Sutton, T. Geussens and M. J. Fairhurst High Frequency Modeling of a Shielded Four-Core Low Voltage Underground Power Cable...................................B. Kruizinga, P. A. A. F. Wouters and E. F. Steennis Comparison of Charge Estimation Methods in Partial Discharge Cable Measurements...........................................................A. R. Mor, P. H. F. Morshuis and J. J. Smit Temperature Dependent Signal PropagationVelocity: Possible Indicator for MV Cable Dynamic Rating ................................................................................................................................Y. Li, P. A. A. F. Wouters, P. Wagenaars, P. C. J. M. van der Wielen and E. F. Steennis Evaluation of Polypropylene/Polyolefin Elastomer Blends for Potential Recyclable HVDC Cable Insulation Applications .................................................................................................................................................................................................Y. Zhou, J. He, J. Hu, X. Huang and P. Jiang

capacITor FIlms Polarization Characteristics of Metallized Polypropylene Film Capacitors under Different Temperatures ..........H. Li, W. Wang, Z. Li, H. Li, B. Wang, F. Lin and Z. Xu Large-Area Dielectric Breakdown Performance of Polymer Films – Part I: Measurement Method Evaluation and Statistical Considerations on Area-Dependence.........................................................................................I. J. Rytöluoto, K. Lahti, M. Karttunen and M. Koponen conDITIon monITorIng X-Ray Fluorescence as a Condition Monitoring Tool for Copper and Corrosive Sulphur Species in Insulating Oil ...............................................................................................P. S. Amaro, M. Facciotti, A. F. Holt, J. A. Pilgrim, P. L. Lewin, R. C. D. Brown, G. Wilson and P. Jarman

ElEcTrIcal TrEEs Comparison and Combination of Imaging Techniques for Three Dimensional Analysis of Electrical Trees ...........................................................................................................................................................................R. Schurch, S. M. Rowland, R. S. Bradley and P. J. Withers Electrical Tree Characteristics in Silicone Rubber under Repetitive Pulse Voltage........................................................................................B. X. Du, T. Han and J. G. Su

FErroElEcTrETs Time Domain Dielectric Relaxation Study on Charging and Discharging Current of Barium Stannate Titanate Ferroelectric Ceramics.....................L. Zhao and X. Wei Effect of SiO2 Doping on the Dielectric, Ferroelectric and Piezoelectric Properties of (Ba0.7Ca0.3)(Zr0.2Ti0.8)O3 Ceramics with Different Sintering Temperatures ................................................................................................................................................................................................................................................W. Liu and S. Li

FIElD mEasUrEmEnTs Electrical Field Computation of Polymeric Insulator Using Reduced Dimension Modeling................................................................................R. Anbarasan and S. Usa Numerical Analysis of the Electric Field In and Near a Bubble Located in One Dielectric in Series with Another .................................................C. Zeng and X. Zheng

gasEoUs DIElEcTrIcs Experimental Investigations of Time Delay Distributions Inside a Commercial Gas Tube ....................................Ĉ. A. Maluckov, M. K. Radović and D. D. Radivojević High Voltage Measuring Apparatus Based on Kerr Effect in Gas...............................................................................T. Kamiya, S. Matsuoka, A. Kumada and K. Hidaka Study on the Influence Mechanism of Trace H2O on SF6 Thermal Decomposition Characteristic Components ................................................................................................................................................................................F. Zeng, J. Tang, X. Zhang, H. Sun, Q. Yao and Y. Miao Effects of Electric Field Distribution and Water Drop Ejection on Flashover of Icicles in Plane-to-Plane Gaps..............................Y. Deng, Z. Jia, Z. Guan and J. Zhou Research and Application of Jet Stream Arc-Quenching Lightning Protection Gap (JSALPG) for Transmission Lines .........J. Wang, J. Liu, G. Wu, Q. Liu and W. Guo Experimental Demonstration of the Effectiveness of an Early Streamer Emission Air Terminal Versus a Franklin Rod ....................................................................................................................................L. Pecastaing, T. Reess, A. De Ferron, S. Souakri, E. Smycz, A. Skopec and C. Stec SF6 Gas Decomposition Analysis under Point-to-Plane 50 Hz AC Corona Discharge..............................................................D. Han, T. Lin, G. Zhang, Y. Liu and Q. Yu FDTD Simulation Considering an AC Operating Voltage for Air-Insulation Substation in Terms of Lightning Protective Level .............................................................................................................................................................................J. Takami, T. Tsuboi, K. Yamamoto, S. Okabe and Y. Baba An Engineering Approach to Calculation of Channel-Base Lightning Current Parameters ............................................................A. A. Reykherdt and K. P. Kadomskaya lIQUID DIElEcTrIcs Estimation of Paper Moisture Content based on Dielectric Dissipation Factor of Oil-Paper Insulation under Non-Sinusoidal Excitations ...........................................................................................................................................................................................A. K. Pradhan, B. Chatterjee and S. Chakravorti Status Assessment of Polymeric Materials in Mineral Oil under Electro-Thermal Aging by Frequency-Domain Dielectric Spectroscopy ...........................................................................................................................................................................................W. Wang, C. Yue, J. Gu, J. Du, F. Li and K. Yang Thermal Aging Effects on the Moisture Equilibrium Curves of Mineral and Mixed Oil-Paper Insulation Systems .................R. Liao, Y. Lin, P. Guo, H. Liu and H. Xia High Thermal Conductivity Transformer Oil Filled with BN Nanoparticles ................................................................................................B. X. Du, X. L. Li and M. Xiao The Minimum Concentration of 1,2,3-Benzotriazol to Suppress Sulfur Corrosion of Copper Windings by DBDS in Mineral Transformer Oils .......................................................................................................................................................N. A. Mehanna, A. M. Y. Jaber, G. A. Oweimreen and A. M. Abulkibash Evaluation of In-Service Oxidative Stability and Antioxidant Additive Consumption in Corn Oil Based Natural Ester Insulating Fluid ..............................................................................................................................................................H. M. Wilhelm, L. Feitosa, L. L. Silva, A. Cabrino and L. P. Ramos

mEasUrEmEnT TEcHnIQUEs Time-Domain Characteristics of the Audible Noise Generated by Single Corona Source under Positive Voltage......................X. Li, X. Cui, T. Lu, D. Zhang and Y. Liu The Effect of Surface Roughness on Corona-Generated Electromagnetic Interference for Long-Term Operating Conductors ................................................................................................................................................X. Bian, Y. Wang, L. Wang, Z. Guan, S. Wan, L. Chen, F. Chen and X. Zhao Development and Evaluation of a New DGA Diagnostic Method Based on Thermodynamics Fundamentals...........V. G. M. Cruz, A. L. H. Costa and M. L. L. Paredes

nanoDIElEcTrIcs The Effect of Resin Stoichiometry and Nanoparticle Addition on Epoxy/Silica Nanodielectrics.........................V. T. Nguyen, A. S. Vaughan, P. L. Lewin and A. Krivda Nanostructured Electrical Insulating Epoxy Thermosets with High Thermal Conductivity, High Thermal Stability, High Glass Transition Temperatures and Excellent Dielectric Properties ..............................................H. Mo, X. Huang, F. Liu, K. Yang, S. Li and P. Jiang oUTDoor InsUlaTIon Energy Absorption Capacity of a 500 kV Surge Arrester for Direct and Multiple Lightning Strokes ................T. Tsuboi, J. Takami, S. Okabe, T. Kido and T. Maekawa

639–648 649–656 657–664

665–672

673–681 682–688 689–700 701–708 709–719 720–727 728–733

734–738 739–746 747–751 752–759 760–765

766–774 775–781 782–788

789–798 799–805

806–814 815–821 822–830

831–841 842–850 851–858 859–863

864–869 870–878 879–887 888–894 895–905 906–915 916–924

(continued on inside)

IEEE DIElEcTrIcs and ElEcTrIcal InsUlaTIon socIETY

The IEEE DIELECTRICS AND ELECTRICAL INSULATION SOCIETY is an organization, within the framework of the IEEE, of members with principal professional interest in dielectrics and in electrical insulation. Members of the IEEE and others are eligible for membership in the Society and will receive this TRANSACTIONS upon payment of the annual Society membership fee and $20.00. For information on joining, write to the IEEE or to the Membership Chairman, as given below. The DEIS WWW home page, at http://sites.ieee.org/deis/ has all available data on the DEIS.

President F. Hegeler Naval Rsrch. Lab/CTI Plasma Phys. Division Washington DC 20375 [email protected] A. Cavallini L. Lamarre S. Ramachandran

2015

Awards & Recognition C. W. Reed 116 Woodhaven Dr. Scotia NY 12302-4808 USA Historian G. Stone Iris Power LP 3110 American Dr. Mississauga Ontario L4V 1T2 Canada

M. Florkowski R. Zarb Y. Wen

R. A. C. Altafim R. Garcia-Colon E. Tuncer

2016

Chapters V. R. Garcia-Colon Instituto de Investigaciones Electricas Cuernavaca Morelos Mexico Magazine R. J. Fleming School of Physics Monash Univ. Victoria, 3800 Australia

Aging Factors H. Zhu Powertech Lab. Inc. 12388-88 Ave. Surrey British Columbia V3W 7R7 Canada

HVDC Cable Systems A. Cavallini Univ. of Bologna Dept. Electr. Eng. Bologna 40136 Italy

VP Administrator P. L. Lewin Electrical Power Engng. Group, ECS University of Southampton Southampton Hampshire, SO17 1BJ United Kingdom

Biodielectrics J. F. Kolb Leibniz Inst. Plasma Sci. Techn. Greifswald, V17489 Germany

Liquid Dielectrics B. Noirhomme IREQ Montreal Quebec Canada

Energy Policy R. C. Wicks DuPont Company 5401 Jefferson Davis Highway Richmond VA 23234

Magazine E. A. Cherney Elect. & Cmp. Eng. Univ. of Waterloo Waterloo Ontario N2L 3G1 Canada

ExEcUTIVE commITTEE officers VP Technical R. Gerhard University of Potsdam Department of Physics Potsdam, D-14469 Germany

adcom members P. C. Gaberson B. G. Stewart A. Tzimas

E. David J. Fothergill S. Lang

2017

Constitution D. L. Schweickart Air Force Research Lab. 2645 Fifth St., Rm D101 MS: AFRL/PRPG Wright Patterson AFB OH 45433 USA Meetings R. Zarb Iris Power LP 3110 American Dr. Mississauga Ontario L4V 1T2 Canada

Education Andrea Cavallini Dept. Elect. Eng. Univ. of Bologna Viale Risorgimento 40136 Bologna Italy

Membership K. Zhou R&D, HV Cable Accessories G&W Electric Co. Bolingbrook IL 60440 USA [email protected]

Technical committee chairs

Nanodielectrics M. F. Frechette Hydro Quebec Res. Inst. Material Sci. 1800 boul. Lionel-Boulet Varennes Quebec J3X 1S1 Canada

Discharges in Air in UHV L. Wang Lab Advanced Tech. Electr. Eng. and Energy Tsinghua University Beijing China

Numerical Methods Applied to Dielectrics F. Baudoin Université Paul Sabatier 31062 Toulouse Cedex France

representatives Sensors G. C. Stone Iris Power LP 3110 American Drive Mississauga Ontario L4V 1T2 Canada

Treasurer W. McDermid Manitoba Hydro 1840 Chevrier Blvd. Winnipeg Manitoba R3T 1Y6 Canada D. Koenig (Germany) N. Nakamura (Japan)

Fellows J. C. Fothergill Pro-Vice Chancellor City University Londong Northampton Square London EC1V 0HB United Kingdom

Nominations M. Farzaneh Elec. Eng. Dept. University du Quebec Chicoutimi Quebec G7H 2B1 Canada

Finance W. McDermid Manitoba Hydro 1840 Chevrier Blvd. Winnipeg Manitoba R3T 1Y6 Canada

A. Calva (Mexico) U. Gäfvert (Sweden)

Gold A. Tzimas Univ. of Manchester School of Elect. Eng. Manchester M13 9PL United Kingdom

Publications Transactions S. Gubanski R. Hackam Chalmers Univ. Tech. 725 N. Talbot Rd. Dept. of Matls. & Mfg. Windsor Goteborg SE-41296 Ont. N9G 1M8 Sweden Canada stanislaw.gubanski@ chalmers.se

Electrohydrodynamics G. Touchard Universite de Poitiers Lab. d’Etudes Aerodynamique Futuroscope-Chasseneuil Cdx. 86962 France

Outdoor Insulation E. Cherney University of Waterloo 200 Univ Ave W. Waterloo N2L 3G1, Waterloo Canada

Past President M. Farzaneh Elec. Eng. Dept. University du Quebec Chicoutimi Quebec G7H 2B1 Canada

Corresponding

Y. Ohki Y. Tanaka N. Frost

administrative committee chairs

Diagnostics M. Florkowski ABB Corporate Research ul. starowislna 13A 31-038 Krakow Poland

Superconductivity D. Swaffield Converteam UK Rugby Warwickshire CV21 1BU United Kingdom

Secretary B. G. Stewart School of Eng. and Built Environ. Glasgow Caledonian Univ. Glasgow Scotland, G4 0BA United Kingdom

Smart Grid V. Catterson Dept. Electrical Electronic Eng. Univ. of Strathclyde Glasgow, G1 1XW United Kingdom

High Fields High Freq. Effects Hulya Kirkici ECE Dept. Auburn University Auburn AL 36849-5201 [email protected]

New Technology J. K. Nelson El., Com., & Sys. Eng. Dept. Rensselaer Polytech Inst. 110 8th St., Rm JEC 5012 Troy NY 12180-3590

Standard Liaison S. Cherukupalli BC Hydro POD B03 6911 Southpoint Dr. Burnaby British Columbia V3N 4X8 Canada IEEE Smart Grid B. Bernstein Consultant 1433 Longhill Drive Rockville MD 20854

The InsTITUTE of ElEcTrIcal and ElEcTronIc EngInEErs, Inc. officers

H. E. Michel (President) B. L. Shoop (President-Elect) S. Sinha (Vice President, Educational Activities) S. Hemami (Vice President, Publication Services & Products)

B. P. Kraemer (President, Standards Association) V. Piuri (Vice President, Technical Activities) J. A. Jeffries (President IEEE-USA) H. Akagi (Division II Director)

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IEEE TRANSACTIONS on DIELECTRICS A PUBLICATION OF THE IEEE DIELECTRICS AND ELECTRICAL INSULATION SOCIETY

April 2015

Volume 22

and Electrical Insulation Number 2

ITDEIS

(ISSN 1070-9878)

PAPERS (CONTINUED)

Experimental Study on the Influence of the Disconnecting Switch Operation on CVTs in UHV Series Compensation Stations..................L. Zhu, S. Ji, J. Li and Y. Liu 925–933 Hydrophobicity, Surface Charge and DC Flashover Characteristics of Direct-Fluorinated RTV Silicone Rubber .....................................................B. X. Du and Z. L. Li 934–940 Characterization of Pre-Flashover Behavior Based on Leakage Current Along Suspension Insulator Strings Covered with Ice .................................................................................................................................................................H. Yang, L. Pang, Z. Li, Q. Zhang, X. Yang, Q. Tang and J. Zhou 941–950 Effect of Grading Ring on Ice Characteristics and Flashover Performance of 220 kV Composite Insulators with Different Shed Configurations ....................................................................................................................................................................L. Shu, S. Wang, X. Jiang, Q. Hu, X. Yang, S. Yang and J. Chen 951–960 Aging Characterization of High Temperature Vulcanized Silicone Rubber Housing Material Used for Outdoor Insulation ........................................................................................................................W. Song, W.-W. Shen, G.-J. Zhang, B.-P. Song, Y. Lang, G.-Q. Su, H.-B. Mu and J.-B. Deng 961–969 Potential Decay on Silicone Rubber Surfaces Affected by Bulk and Surface Conductivities ...................................................S. Alam, Y. V. Serdyuk and S. M. Gubanski 970–978 Impact of Different Fillers and Filler Treatments on the Erosion Suppression Mechanism of Silicone Rubber for Use as Outdoor Insulation Material ..................................................................................................................................................................................................S. Ansorge, F. Schmuck and K. O. Papailiou 979–989 Elaboration of Novel Image Processing Algorithm for Arcing Discharges Recognition on HV Polluted Insulator Model........A. K. Chaou, A. Mekhaldi and M. Teguar 990–999 Effects of Pollution Materials on the AC Flashover Performance of Suspension Insulators ........................................................Z. Zhang, D. Zhang, X. Jiang and X. Liu 1000–1008 Intrinsic-Like Surface Flashover Voltage of Insulators ...............................................................J. Cheng, J. Su, X. Zhang, B. Zeng, X. Wu, L. Wang, J. Fang and X. She 1009–1014 PARTIAL DISCHARGES A New Image-Oriented Feature Extraction Method for Partial Discharges ....................K. Wang, J. Li, S. Zhang, F. Gao, H. Cheng, R. Liu, R. Liao and S. Grzybowski Partial Discharge Characteristics in Composite Insulation Systems with PPLP® for HTS Cable ...................................................................................................................Y. Kikuchi, K. Yamashita, S. Matsuoka, A. Kumada, K. Hidaka, K. Tatamidani and T. Masuda On-Line Partial Discharge Source Location in Single-Core Cables with Multi Sheath-Ground Connections............................................A. Babaee and S. M. Shahrtash A Hybrid Algorithm Based on S Transform and Affinity Propagation Clustering for Separation of Two Simultaneously Artificial Partial Discharge Sources ......................................................................................................................................K. Wang, J. Li, S. Zhang, R. Liao, F. Wu, L. Yang, J. Li, S. Grzybowski and J. Yan Partial Discharge Pattern Recognition via Sparse Representation and ANN .................................................M. Majidi, M. S. Fadali, M. Etezadi-Amoli and M. Oskuoee Partial Discharges in a Cavity Embedded in Oil-Impregnated Paper: Effect of Electrical and Thermal Aging .............................................................................................................................................................M. G. Niasar, N. Taylor, P. Janus, X. Wang, H. Edin and R. C. Kiiza Partial Discharge Recognition in Gas Insulated Switchgear Based on Multi-Information Fusion..........................................................................L. Li, J. Tang and Y. Liu Partial Discharge Inception Electric Field Strength of Water Droplets on Polymeric Insulating Surfaces ..............................................M. H. Nazemi and V. Hinrichsen A Semi-Definite Relaxation Approach for Partial Discharge Source Location in Transformers ..................................................F. Zeng, J. Tang, L. Huang and W. Wang Probabilistic Wavelet Transform for Partial Discharge Measurement of Transformer.........................................................................................J. Seo, H. Ma and T. Saha

SOLID DIELECTRICS Prediction of Residual Breakdown Electrical Field Strength of Epoxy-Mica Paper Insulation Systems for the Stator Winding of Large Generators .............................................................................................................................................................................................K. Tanaka, H. Kojima, M. Onoda and K. Suzuki Evolutions of Surface Characteristics and Electrical Properties of the Fluorinated Epoxy Resin during Ultraviolet Irradiation .......................................................................................................................................................................Z. An, Q. Yin, H. Xiao, D. Xie, F. Zheng, Q. Lei and Y. Zhang Investigation of the Breakdown Process of Syntactic Foam under Lightning Impulse Stress at Liquid Nitrogen Temperature......D. Winkel, R. Puffer and A. Schnettler Dielectric and Electrical Properties of Radiation-Cured Epoxy.................................B. Vissouvanadin, G. Teyssedre, S. Le Roy, C. Laurent, G. Ranoux and X. Coqueret Permittivity and Electrical Breakdown Response of Nylon 6 to Chemical Exposure...............................................................................................R. Ding and N. Bowler Mathematical Model for Numerical Simulation of Current Density in Microvaristor Filled Insulation Materials .............................................S. Blatt and V. Hinrichsen Influence of Temperature on Electrical Aging Characteristics of Polytetrafluoroethylene Under Nanosecond Pulses.......................T. Wang, J. Wang, P. Yan and J. Ran Epoxy/BN Micro- and Submicro-Composites: Dielectric and Thermal Properties of Enhanced Materials for High Voltage Insulation Systems ................................................................................................................................................................................................................T. Heid, M. Fréchette and E. David Leader Propagation Models of Ultrahigh-Voltage Insulator Strings based on Voltage/Time Curves under Negative Lightning Impulses at High Altitude ......................................................................................................................................................................................................Y. Hao, Y. Han, L. Tang, C. Mao and L. Li Characteristics of Streamer Propagation Along the Insulation Surface: Influence of Dielectric Material........X. Meng, H. Mei, C. Chen, L. Wang, Z. Guan and J. Zhou SPACE AND SURFACE CHARGES Effects of Space Environmental Exposure on Photoemission Yield of Polyimide ..................J. Wu, A. Miyahara, A. Khan, M. Iwata, K. Toyoda, M. Cho and X. Zheng Recovery Algorithm for Space Charge Waveform under Temperature Gradient in PEA Method ....................................................H. Wang, K. Wu, Q. Zhu and X. Wang Nonlinear Relaxational Polarization in Aluminum Oxide ..................................................................................................................L. Kankate, A. Gratsov and H. Kliem Stepwise Electric Field Induced Charging Current and Its Correlation with Space Charge Formation in LDPE/ZnO Nanocomposite ...........................................................................................................................................................................................F. Tian, J. Yao, P. Li, Y. Wang, M. Wu and Q. Lei Determination of Charge-Trapping Sites in Saturated and Aromatic Polymers by Quantum Chemical Calculation ......................................................................................................................................................T. Takada, H. Kikuchi, H. Miyake, Y. Tanaka, M. Yoshida and Y. Hayase

TRANSFORMERS Influence of the Winding Design of Wind Turbine Transformers for Resonant Overvoltage Vulnerability ......................A. H. Soloot, H. K. Ho/idalen and B. Gustavsen Examining the Ageing of Transformer Insulation Using FRA and FDS Techniques ..........................................................M. F. M. Yousuf, C. Ekanayake and T. K. Saha Method to Evaluate the Degradation Condition of Transformer Insulating Oil – Establishment of the Evaluation Method and Application to Field Transformer Oil ...............................................................................................................J. Wada, G. Ueta, S. Okabe and T. Amimoto A New Technique to Measure Interfacial Tension of Transformer Oil using UV-Vis Spectroscopy......................N. A. Bakar, A. Abu-Siada, S. Islam and M. El-Naggar Modelling the Dielectric Response Measurements of Transformer Oil......................................................................................K. Bandara, C. Ekanayake and T. K. Saha Design and Construction of a ±100 kV Gas Switch for Linear Transformer Drivers .............................................................................J. Jiang, J. Liu, M. Liu and M. He

1015–1024

1025–1030 1031–1041

1042–1060 1061–1070

1071–1079 1080–1087 1088–1096 1097–1104 1105–1117 1118–1123

1124–1133 1134–1141 1142–1150 1151–1160 1161–1170 1171–1175

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1186–1192 1193–1203

1204–1212 1213–1219 1220–1231 1232–1239

1240–1249

1250–1257 1258–1265

1266–1274 1275–1282 1283–1291 1292–1297

VACUUM INSULATION 3-D SEEA Charge Analyses on Surface of Insulators in Vacuum ...........................................................................T. Umemoto, Y. Shimizu, H. Naruse and O. Yamamoto 1298–1305 Effect of High-Frequency High-Voltage Impulse Conditioning on Inrush Current Interruption of Vacuum Interrupters ....Y. Zhang, H. Yang, Y. Geng, Z. Liu and L. Jin 1306–1313

CORONA The Correlation between Audible Noise and Corona Current in Time Domain Caused by Single Positive Corona Source on the Conductor .........................................................................................................................................................................................X. Li, X. Cui, T. Lu, Y. Liu, D. Zhang and Z. Wang 1314–1320 Experimental Investigation of Corona Onset in Contaminated Polymer Surfaces..........................................................................................A. L. Souza and I. J. S. Lopes 1321–1331

1042

Ke Wang et al.: A Hybrid Algorithm Based on S Transform and Affinity Propagation Clustering for Separation

A Hybrid Algorithm Based on S Transform and Affinity Propagation Clustering for Separation of Two Simultaneously Artificial Partial Discharge Sources Ke Wang, Jinzhong Li, Shuqi Zhang China Electric Power Research Institute Haidian District, Beijing, P. R. China, 100192

Ruijin Liao, Feifei Wu, Lijun Yang, Jian Li State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University, Chongqing, P. R. China, 400044

Stanislaw Grzybowski High Voltage Laboratory, Department of Electrical and Computer Engineering Mississippi State University, Mississippi State, MS 39762, USA and Jiaming Yan School of Electric Power Engineering China University of Mining and Technology, Xuzhou, P. R. China, 221116

ABSTRACT This paper presents a hybrid algorithm for separation of two simultaneous partial discharge (PD) sources of oil-paper insulation based on S transform (ST) and affinity propagation clustering (APC). Similarities between PD pulses are acquired by comparisons of the corresponding ST-amplitude (STA) matrices, which are input of APC to realize the PD pulses separation and obtain two sub-groups of PD pulses having similar time-frequency characteristics. A classification-based model for separation results validation are developed using a support vector machine with particle swarm optimization (PSO-SVM) classifier and 27 phase-resolved partial discharge (PRPD) statistical features. Artificial defect models are made to simulate two PD sources simultaneously active. Several PD data of different two simultaneous PD sources are acquired in laboratory and adopted for algorithm testing. It is shown that ST computes very fast and is suitable for online PD applications. The separation results of PD data produced in laboratory are verified by the developed validation model, which demonstrate that ST combined with APC can effectively eliminate pulse-shaped noises (PSN) and separate pulses of two simultaneous PD sources. Comparisons with typical separation methods from the state of the art provide better separation performance of the proposed ST combined with APC algorithm for two simultaneous PD sources. The obtained results in this work provide a solid basis for the data mining technique that can be used to facilitate PD diagnosis of transformers. Index Terms - Partial discharges, pulses separation, S transform, time-frequency similarity, affinity propagation clustering, diagnostics, oil-paper insulation

1 INTRODUCTION OIL-IMMERSED power transformers are the key equipment in the power grid, whose safe operation significantly affects the reliability of electrical power supply. However, defects that often give rise to partial discharge (PD) would unavoidably Manuscript received on 20 May 2013, in final form 31 May 2014, accepted 14 August 2014.

occur within the insulation system during apparatus manufacture, transportation and long-term service, strongly threatening the safe running of transformers. Thus, PD measurements and diagnosis may constitute a power tool for identification of the insulation defects. With rapid development of artificial intelligence and digital signal processing technologies, much attention has been paid to the classification and recognition of partial discharges, which was mainly concentrated on identifying defect types based on the

DOI 10.1109/TDEI.2014.004117

IEEE Transactions on Dielectrics and Electrical Insulation

Vol. 22, No. 2; April 2015

fingerprint database derived from artificial defect models in laboratory [1-3]. However, more than two PD sources due to the presence of various defects and pulse-shaped noises (PSN) may simultaneously occur in power transformers filled with complex liquid-solid insulation system. Besides, PD may also happen on bushing surface owing to the tough service environments. What mentioned above often makes the recorded PD patterns mixed by that from several PD sources simultaneously active and leads to fully or partially overlapped patterns in traditional PRPD methods. To solve the key problem, separation procedure is strategically adopted to avoid critical misinterpretation and incorrect diagnosis of the mixed PD patterns, which is based on the assumption that the same PD source generates pulses having similar waveshapes while different PD sources are characterized by different waveshapes [4]. Accordingly, the obtained sub-patterns after separation can be further identified by traditional PD pattern diagnosis methods. The separation and identification of more than two PD sources within power transformers needs a digital PD measurement system which allows the acquisition of the whole pulses with a high enough sampling rate to keep sufficient waveshape information on the registered PD pulses. Till now several attempts have been concentrated on the separation of pulses from different PD sources including mixed Weibull model [5], equivalent time-frequency analysis (ETFA) [6-10], normalized auto-correlation function (NACF) [4, 11], amplitude-frequency space (AFS) [12], wavelet decomposition (WD) combined with principal component analysis (PCA) [13], envelope comparison [14], blind source separation (BSS) algorithm [15], 3-phase-amplitude-relation-diagram (3PARD) method [16-18], etc. These methods have already been validated by both experiments in laboratory and field testing, which were acknowledged to be quite effective with the assistance of human investigations. A typical example lies in the most effective approach ETFA which is characterized by equivalent time T and equivalent bandwidth F. Signals coming from different PD sources are substantially grouped in the T-F plane. However, the separation procedure is still further implemented by additional clustering algorithm and usually the cluster number should be determined by visual inspections. This work attempts a new method for separation of two simultaneous PD sources employing S transform (ST) and affinity propagation clustering (APC), which was reported for PD pulses separation of oil-paper insulation [19]. However, the PD data for algorithms validation are artificially combined using pulses produced by single PD defects although comparatively good separation results were confirmed. With an extension of works in [19], PD data sampled from two simultaneous partial discharge sources are further used to test the proposed separation algorithm. The separation results are validated by a classification model based on support vector machine (SVM). The rest of the present paper is organized as follows. Section 2 shows some details of the proposed separation method. In Section 3, the validation model for separation results is outlined. Section 4 gives a description of the PD experimental arrangement. The validation results are detailed in Section 5, followed by a further discussion in Section 6. Section 7 concludes and gives the summary. Finally, Section 8 presents the appendix.

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2 THE PROPOSED ALGORITHM FOR SEPARATION OF TWO SIMULTANEOUS PD SOURCES The proposed separation method for two simultaneous PD sources is reported in Figure 1. The single PD pulses and the relevant φ-q data are recorded, respectively. Then, ST is used to acquire the time-frequency information of original PD pulses, and a similarity matrix between different PD pulses is obtained based on the ST-amplitude (STA) matrix of each PD pulse. By using STA based similarity matrix, the pulses of two simultaneous PD sources can be grouped by a new clustering algorithm APC, and thereby two phase-resolved partial discharge (PRPD) sub-patterns labeled as #1 and #2 are further derived and validated.

Figure 1. Flowchart of the proposed method.

2.1 S TRANSFORM OF PD PULSES The separation method proposed in this work is based on the similarity between the ST-amplitude matrices of PD pulses belonging to a set of N pulses. ST is a time-frequency representation technique [20], which can be described as 

f



2

S  , f    x(t )

e



( t  )2 f 2 2

e j 2 ft dt

(1)

where x(t) is the recorded PD pulse, and τ is a parameter that controls the location in the timeline. The STA matrix is shown as

A  , f   S  , f 

(2)

The advantages of using ST over original PD pulses lie in that STA matrix can effectively extract the joint time-frequency information of PD pulses, which is superior to time or frequency description alone. Moreover, PD pulses often have obvious polarity. It means that there are both positive pulses and negative pulses in a power cycle which often have inverse waveshape properties. STA matrix can eliminate the objective polarity influence of original pulses due to the fact that it projects the original PD pulses to amplitude levels in the time-frequency plane.

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A normalization method is adopted to eliminate the magnitude dispersion, shown as

xn (t ) 

x (t )

(3)

x(t ) )

max(

where x(t) represents the recorded PD pulse, max(|x(t)|) gives the maximum absolute value of x(t), and xn(t) indicates the normalized PD pulse. The ST time-frequency representations are all based on the normalized PD pulses in this work. Figure 2 gives a typically registered PD pulse and the relevant ST time-frequency representation after pulse normalization.

0.25

Frequency(MHz)

Amplitude(V)

m

50

0.5

0 -0.25 -0.5 0

the appropriate preference boundaries pmax and pmin when p(i) is assumed as the same value, and thereby APC generates only one cluster with p(i) = pmin and produces the largest clusters, i.e. the sample number, with p(i) = pmax. In the current study, APC is employed to achieve pulses separation of two simultaneous PD sources, in which the similarity matrix S of PD pulses needs to be calculated first. As been reported, STA matrix is used to represent original pulses, and we compute the STA-based similarities namely what we define as time-frequency similarities between PD pulses using the following expression

2.5E+3 Time(ns)

(a)

5.0E+3

S  i, j  

40 30 20 10 0

2.5E+3 Time(ns)

3 5.0E+

(b)

Figure 2. A typical example of PD pulse and its ST time-frequency representation: (a) PD pulse, (b) ST time-frequency representation.

2.2 AFFINITY PROPAGATION CLUSTERING USING STA-BASED SIMILARITY Since PD pulses separation is actually a clustering process with the purpose of achieving the relevant pulses belonging to the same PD source. Affinity propagation clustering algorithm, originally proposed by Frey [21], is a novel clustering method based on the similarities between data points. APC considers all the data points as the potential cluster exemplars, and finally pick up the centroids and the corresponding samples belonging to each cluster automatically through message propagation procedures. Compared with traditional clustering algorithms, APC has some advantages shown as follows: (1) APC is not sensitive to the initial exemplars due to the fact that it considers all the data points as potential exemplars, (2) APC can present the cluster numbers automatically according to the similarity matrix between data points and “preference” that is an important parameter representing the possibility of a data point being exemplar and (3) APC computation results more than once are comparatively stable and it is appropriate for large data processing. Due to the above superiorities, APC has been widely applied in image segmentation [22], gene recognition [23], text clustering [24] and other various applications [25-26]. Assume that STA matrices dataset of PD pulses is A = {A1, A2, …, AN}, where Ai represents STA matrix of the ith PD pulse, N is the pulse number. The main parameters of APC are similarity matrix S, preference p and damping factor λ. The introduction of λ, usually equal to 0.5 in most cases, reduces the oscillations during message-passing procedures. p(i) implies the preference that data point i should be chosen as a centroid, and it strongly influences the cluster number generated by APC. In APC computation, p(i) is assigned as S(i, i) that is the diagonal of similarity matrix S. More larger of S(i, i), the possibility of the ith sample to be an exemplar is bigger. If the prior knowledge is unknown, we can assign p(i) as the same value indicating the possibilities of each data point to be the exemplars are equal [27]. Frey also presented

n

  A (k , l )  A  A (k , l )  A  k 1 l 1

i

i

j

j

2  m n 2  m n A ( k , l )  A   i   i    Aj (k , l )  Aj    k 1 l 1  k 1 l 1 

(4)

where S(i, j) is the time-frequency similarity between the ith and jth PD pulse, m and n are the row and column number of STA matrix, Ai and Aj are mean values of STA matrices Ai and Aj.

3 VALIDATION OF SEPARATION RESULTS BASED ON A CLASSIFICATION MODEL 3.1 VISUAL INSPECTIONS The PRPD patterns of two simultaneous PD sources are often complex due to the fact that a unique PRPD distribution of each PD source is confirmed both in laboratory and field testing. In this work, the pulses of two simultaneous PD sources are separated to two sub-groups using the proposed ST combined with APC algorithm. The first-step validation can be performed by visual inspections on the obtained PRPD sub-patterns, to check whether the complex PRPD pattern are transformed to two comparatively simple and regular PRPD sub-patterns. In addition, the typical pulse waveshape of each sub-group also provides valuable references for visual validation. It should be mentioned that the visual validation can be achieved by well-skilled engineers but not fitful for all circumstances. Thus, a further computer-based method for automatic and convincing validation of the separation results should be developed. 3.2 SEPARATION VALIDATION USING CLASSIFICATION-BASED METHOD In order to accurately verify the effectiveness of the proposed separation technique, a computer-based validation is developed using a classification model. If the separated PRPD sub-patterns are correctly classified into its intrinsic category, the separation

Figure 3. Flowchart of separation results validation.


Vol. 22, No. 2; April 2015

results can be validated. Thus, a classifier should be trained by PD data that are sampled from single PD sources. Besides, feature extraction should be implemented to represent original PD data and facilitate classifier training. The validation flowchart of the validation model is summarized in Figure 3. 3.3 FEATURE EXTRACTION OF PD PULSES Feature extraction aims at obtaining low-dimensional representation of original PD data, which can simplify the classification model. PRPD pattern-based fingerprints are proved to be one of the most effective approaches for PD representation, which is derived from a few 2-dimensional (2D) statistical histograms, shown in Figure 4. The most representative 2D histograms used to extract PD fingerprint in this study are given as follows: (1) Hqmax(φ), indicating the peak discharge amplitude distribution over the discharge phase, (2) Hqave(φ), indicating the average discharge amplitude over the discharge phase, (3) Hnφ), indicating the discharge number distribution over the discharge phase, (4) Hn(q), indicating the discharge number distribution over discharge amplitude.

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typically statistical operators represented by kurtosis (ku), skewness (sk), the number of peaks (peak), asymmetry (asy) and cross-correlation coefficient (cc) are used to quantify the shape of the above mentioned four PRPD spectra (marked in Table 1). Each parameter indicates a certain sort of information hidden in PD spectra. The specific definitions of these statistical parameters are described in [28-29]. 3.4 CLASSIFICATION BASED ON SUPPORT VECTOR MACHINE SVM is a machine learning technique based on statistical theory and structure risk minimization principle, which overcomes such defects as dimensionality curse and over-fitting that are quite easy to occur to some other classifiers. SVM is originally designed for binary classification problems [30], and any classification problems can be restricted to the binary classification problem without loss of generality. When SVM is employed for multi-class classification, it needs to expand binary SVM to multi-class SVM using the encoding strategies including one-against-all (OAA), one-against-one (OAO), error-correcting-output-codes (ECOC) and minimal output coding (MOC), etc [31]. A well-known SVM toolbox LIBSVM developed by Lin [32] is employed, in which OAO expansion strategy is adopted. LIBSVM is an integrated toolbox for solving various problems such as classification (C-SVC, NU-SVC), regression (EPSILON-SVR, NU-SVR) and distribution estimation (ONE-CLASS). Several kernel functions are also included in the software. In the present work, RBF kernel function is applied to construct SVM, which is given by

K ( x, y )  exp( x  y

2

2 2 )

where the kernel parameter σ is a positive real number. Before training of SVM, a few parameters need to be determined first. The kernel parameter σ and penalty factor c are the most important parameters which would strongly influence SVM classification performances. In this work, SVM parameters σ and c are optimized by particle swarm optimization (PSO) which was originally proposed by Kennedy and Eberhart [33]. The particles within the swarm are composed by σ and c. The fitness of each particle is calculated by the classification accuracy obtained by 5-fold cross validation (CV) on the training samples. The flowchart of PSO optimization is shown in Figure 5.

(a)

Set up the parameters of PSO

(b) Figure 4. PRPD pattern and its statistical spectra of partial discharge: (a) PRPD pattern, (b) statistical spectra derived from PRPD pattern.

Statistical parameters ku sk peak asy cc

Table 1. Statistical features of PRPD spectra. Hqave(φ) Hn(φ) Hqmax(φ) ＋－＋－＋－ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

(5)

Hn(q) √ √ √

It has been confirmed that the shapes of PRPD histograms are strongly connected with defect types, which can be quantitatively described by many statistical parameters. In this work, 27

Randomly initialize x i and vi Train the SVM classifier Calculating the fitness f(xi) of each particle Updata the velocity and position of the particles Max iteration?

Produce a new population of particles

Obtain optimal parameters of SVM

Figure 5. Flowchart of parameters optimization of SVM with PSO.

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4 PD EXPERIMENTS FOR SEPARATION ALGORITHM VALIDATION 4.1 MEASUREMENT OF TWO SIMULTANEOUS PD SOURCES A digital measurement system is established in laboratory to acquire PD signals, shown in Figure 6. A 50 Hz discharge-free high voltage source is used, and a capacitor divider composed by C1 and C2 is connected to the oscilloscope displaying the applied high voltage. Cx represents the test object, Ck is a discharge-free coupling capacitor, Zd is the pulse current detector. The original PD signals are measured and recorded by LeCroy Wavepro 7100 that is a digital oscilloscope with the maximum sampling rate 20 GS/s and 1 GHz bandwidth. The sampling rate for recording PD data is 100 MS/s.

relevant to each PD source are shown in Figure 8. All the electrode systems are surrounded by transformer oil. Transformer Oil HV electrode Pressboards ɮ5.6mm

8mm

HV electrode

Insulating clamp

ɮ25mm

8mm

d

ɮ60mm

ɮ60mm

(a)

(b)

HV electrode

HV electrode

R200ȝm

R Lecory Wavepro 7100 HVAC source

C1

Cx

8mm

Ck

Zd

C2

h1 h2

l

8mm

ɮ60mm

ɮ60mm

(c)

(d)

Figure 8. Artificial defect models: (a) cavity discharge within pressboard, (b) surface discharge in oil, (c) corona discharge in oil, (d) discharge in oil/air interface.

Figure 6. Schematic diagram for PD measurement. High voltage

Insulating base

Figure 7. The test object of two simultaneous PD sources.

In the current study, we combine two defect models together to simulate two PD sources simultaneously active shown in Figure 7. The combined two PD defects are used to replace the test object Cx in Figure 6. When the applied voltage is over inception voltages both of the two defects, PD would simultaneously happen on the two defects. PD waveshapes and the relevant φ-q data are acquired through the oscilloscope. Four artificial defect models are produced to form different two simultaneous PD sources within power transformers [34-36] including internal cavity discharge, surface discharge in oil, corona discharge in oil and discharge in oil/air interface, which are expressed as G, S, C and I for short. The electrode structures

Figure 8a illustrates the source G composed by three layers: the upper and bottom layers, which are 1 mm thick Kraft pressboards, and the middle layer with the thickness of h = 0.2 mm. A Ф2 mm circle is cut out at the center of the middle paper, and the three layers are affixed together with silica gel to form a sealed cavity (sandwich structure). As shown in Figure 8b, source S is made by a rod-to-plane electrode system. The diameter of the upper rod electrode is D = 25 mm. The Kraft pressboard placed on the plane electrode is 2 mm thick. Source C is simulated by a needle-to-plane electrode system in Figure 8c. The distance between the needle tip with the curvature radius of 200 μm and the 1 mm thick pressboard is l = 1 mm. In Figure 8d, the discharge in oil/air interface is simulated by plane-to-plane electrode system. The dielectrics between the two electrodes are composed by oil and air, and the discharge would happen on the air/oil interface. The distance between the interface and the upper electrode is h1 = 5 mm, and the distance between the interface and the 2 mm thick pressboard upon the bottom electrode is h2 = 10 mm. The diameters of the circular Kraft pressboards are all 80 mm. New pressboards and oil are used in this work, which are all dried and impregnated with oil in a vacuum tank. More details of the pretreatment procedures can be found in [37-38]. The moisture content after pretreatment in pressboard is about 0.3%-0.4%, and oil moisture is less than 10 ppm. All the experiments are performed under room temperature (20-25ºC). During the whole experiments, all the pressboards and oil samples are preserved in a sealed container after the pretreatment. 4.2 VALIDATION TOOL ESTABLISHMENT In order to train the classification model, PD samples belonging to each single PD source are sampled in laboratory. For each PD source, two defect sizes are pre-formed, and PD


Vol. 22, No. 2; April 2015

data of all defect models are acquired under two voltages. The obtained PD samples are given in Table 2 where 80 samples of each PD type and the relevant PRPD patterns are saved. 27 statistical features reported in Table 1 are extracted to generate the training dataset for PSO-SVM classifier. The relevant parameters of SVM and PSO are listed in Table 3. Table 2. PD samples sampled from single defect for separation validation. Partial discharge defect types Sampling voltage / PD samples Defect types Defect sizes h = 0.2 mm 8 kV / 20 samples 16 kV / 20 samples G h = 0.5 mm 8 kV / 20 samples 16 kV / 20 samples D = 12 mm 22 kV / 20 samples 25 kV / 20 samples S D = 25 mm 22 kV / 20 samples 25 kV / 20 samples l = 1 mm 18 kV / 20 samples 21 kV / 20 samples C l = 3 mm 22 kV / 20 samples 25 kV / 20 samples 27 kV / 20 samples 30 kV / 20 samples h1 = 5 mm I h1 = 10 mm 29 kV / 20 samples 31 kV / 20 samples Table 3. Parameters of SVM and PSO. Parameters Initial values 103 cmax -3 c 10 min SVM σmax 103 σmin 10-3 wmax 0.9 wmin 0.4 Tmax 200 m 20 PSO c1 1.5 c2 1.7 vmax 5 vmin -5 where cmax and cmin control the numerical boundary of penalty factor c, σmax and σmin give the numerical boundary of kernel parameter σ, wmax and wmin are the numerical range of inertial weight, Tmax represents the maximum generations, m is the population size. Algorithms

Fitness curve - Accuracy[PSOmethod] Best c = 248.3 g = 0.85, CVAccuuracy = 97.2%

100

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5 VALIDATION RESULTS To examine the effectiveness of the proposed separation algorithm for two simultaneous PD sources within oil-paper insulation, four examples associated with different two simultaneous PD sources are investigated in the following sections. 5.1 PULSE-SHAPED NOISES REJECTION Suppression of pulse-shaped noises (PSN) is often a challenge in PD monitoring due to the fact that PSN waveshapes usually have the characteristic of oscillatory decay which is similar with PD pulses. Using traditional noise-rejection methods such as wavelet de-nosing, PSN cannot be eliminated effectively resulting in the occurrence of PSN in the recorded PRPD patterns. A possible solution to this problem can be derived from the proposed separation algorithm, in which PSN is regarded as a hypothetic discharge source. The method is based on the assumption that different waveshapes and frequency characteristic are existed between PD pulses and PSN. In this way, ST combined with APC gives the separation results of the two discharge sources including PD and PSN to generate the collection of PD pulses, and simultaneously eliminate PSN from the acquired pulses. Figure 10 reports the PRPD pattern composed by N = 2077 pulses relevant to G-type defect under 8 kV. It can be easily found that a group of pulses are gathered around 110º whose phase distribution differs from common PDs. We calculate time-frequency similarity matrix S of the acquired 2077 pulses, and the relevant pmax = 0.9 and pmin = -374 are obtained. Set p(i) = -100 and input S and p, APC generates two clusters of 2077 pulses labeled as group #1 and #2. The PRPD sub-patterns, PD pulses and the corresponding time-frequency representations of each centroid, relevant to group #1 and #2, are given in Figure 11.

90

Fitness

80 70 60 50 Best fitness Average fitness

40 30

0

50

100 Generations

150

Figure 10. PRPD pattern of G-type defect model. 200

Figure 9. The fitness developments of PSO for training of SVM.

Based on the obtained PD training dataset, the kernel parameter σ and penalty factor c of SVM are optimized by PSO connected with classifier training. The fitness developments of PSO during the optimization process are given in Figure 9. It can be obtained that a good CV classification accuracy of 97.2% is earned with c = 248.3 and σ = 0.85. The classification results of training dataset clearly illustrate that PSO-SVM with 27 PRPD statistical features as input vector can effectively validate the separation results of two simultaneous PD sources.

The PRPD sub-patterns in Figure 11a and 11b provide different phase distribution. Group #1 locates in 0º-90º, 160º-270º and 340º-360º while group #2 converges around 110º. Therefore, based on visual inspections, ST combined with APC seems to present a comparatively good result for PSN suppression. It can also be confirmed, by the pulse waveshape relevant to group #2 in Figure 11d, that the frequency of PSN is remarkably lower than PD pulse in Figure 11c in spite of its oscillating decay characteristic. Meanwhile, the differences of time-frequency representations between the pulses relevant to group #1 and #2 are also confirmed by Figures 11e and 11f. With careful examinations PSN associated with group #2 are primarily judged as disturbances coming from thermostatic oil

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27 statistical features of PRPD sub-pattern relevant to group #1 are shown in Figure 12. PSO-SVM classifier makes a decision as G type, which coincides with the real experiment defect model. The above results report a clear validation of ST combined with APC for PSN suppression.

(b)

0.2

0.1

0.1

0.05

Amplitude(V)

Amplitude(V)

(a)

0

0

-0.05

-0.1 -0.2 0

5.2 SEPARATION OF INTERNAL PD AND SURFACE PD IN OIL PD measurements are carried out on a test object consisted of G-type and S-type defects by means of the digital measurement system. N = 3481 pulses with 5 μs width are recorded under 20 kV, and the obtained PRPD pattern is given in Figure 13. By visual inspections, the complex PRPD pattern in Figure 13 may include two different parts: pulses with large amplitude and pulses with low amplitude.

2.5E+3 Time(ns)

-0.1 0

5.0E+3

(c)

2.5E+3 Time(ns)

5.0E+3

(d)

40

Frequency(MHz)

Frequency(MHz)

50

30 20 10

40 30 20 10

Figure 13. PRPD pattern of the mixed defect models consisting of G and S. 0

2.5E+3 Time(ns)

5.0E+3

(e)

0

2.5E+3 Time(ns)

5.0E+3

(f)

Figure 11. PRPD sub-patterns associated with their centroid’s pulses and time-frequency representations of two clusters of Figure 10: (a), (c) and (e) are of group #1, (b), (d) and (f) are of group #2.

bath (TOB) which is often employed for electro-thermal aging experiments in our laboratory. The results displayed in the oscilloscope reports that PSN occurs and disappears accordingly with the shut off and turn on of TOB. Actually, an electrical machinery is equipped within TOB for oil circulation, which may produce the acquired PSN that can be coupled into PD measurement system by the grounding system. Hence, the PRPD sub-pattern of group #2 is visually judged as PSN.

(b) 2

Amplitude(V)

0.2 0.1 0 -0.1 -0.2 -0.3 0

2.5E+3 Time(ns)

1 0 -1 -2 0

5.0E+3

(c) 50

40

40

30 20 10 0

2.5E+3 Time(ns)

(e)

2.5E+3 Time(ns)

5.0E+3

(d)

50 Frequency(MHz)

Figure 12. Statistical features of the pulses relevant to group #1 in Figure 11 identified as G.

(a) 0.3

Amplitude(V)

1.1298 1.0274 3.3440 2.7464 24.0000 24.0000 0.7437 0.7082 0.9445 1.0251 2.7728 2.8213 26.0000 24.0000 0.8020 0.7088 1.5588 1.4716 5.3772 3.9655 16.0000 16.0000 0.5199 0.5012 1.4042 6.0875 51.0000

Frequency(MHz)

sk+ skku+ kupeaks+ peaksasy cc sk+ skku+ kupeaks+ peaksasy cc sk+ skku+ kupeaks+ peaksasy cc sk ku peaks

5.0E+3

30 20 10 0

2.5E+3 Time(ns)

5.0E+3

(f)


Vol. 22, No. 2; April 2015

(a)

(b)

Figure 15. Statistical features of the pulses relevant to each group of Figure 14: (a) is group #1, (b) is group #2, and identified as G and S, respectively.

27 statistical features extracted from the PRPD sub-patterns relevant to group #1 and #2 in Figures 14a and 14b are shown in Figures 15a and 15b, respectively. The classification results of group #1 and group #2 by PSO-SVM are G and S which validate the proposed separation technique again.

Implementing the same computation procedures with the above mentioned examples, we obtain the time-frequency similarity matrix S, and the relevant preference boundaries pmax = 0.98 and pmin = -726. In this case, p(i) = -100 is also chosen to perform APC, which generates two groups of pulses labeled as #1 and #2. The PRPD sub-patterns of each group are derived and

(a)

(b)

0.2

0.8 0.6

0.1

Amplitude(V)

0.0880 0.0312 2.0654 1.9824 21.0000 27.0000 1.2612 0.7881 0.1073 0.0422 1.9490 1.8791 23.0000 27.0000 1.2045 0.7562 0.1204 0.0959 2.2508 2.3199 18.0000 21.0000 1.1816 0.6901 0.7910 3.5969 53.0000


Figure 16. PRPD pattern of the mixed defect models consisting of G and C

0 -0.1 -0.2 0

0.4 0.2 0

2.5E+3 Time(ns)

5.0E+3

-0.2 0

(c) 50

40

40

30 20 10 0

2.5E+3 Time(ns)

(e)

2.5E+3 Time(ns)

5.0E+3

(d)

50 Frequency(MHz)

0.7351 0.6673 2.4147 2.2221 29.0000 29.0000 0.8287 0.6813 0.6649 0.6276 2.2296 2.2681 26.0000 28.0000 0.9627 0.7715 0.9565 0.8842 2.9123 2.3959 24.0000 25.0000 0.6025 0.6189 1.7201 8.0138 47.0000


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5.3 SEPARATION OF INTERNAL PD AND CORONA IN OIL Another example is fabricated by positioning the G-type and C-type defect models between the circular high-voltage plane and the insulating base to generate two simultaneous PD sources due to internal discharge and corona in oil. The PD data are recorded at an applied voltage of 18 kV, and the acquired PRPD pattern, which is composed by N = 3227 pulses, is presented in Figure 16.

Amplitude(V)

The time-frequency similarity matrix S is then calculated using equation (4). The preference boundaries obtained based on S are pmax = 0.98 and pmin = -424. Applying p(i) = -100, the application of APC to label the clusters of Figure 13 produces two different groups (#1 and #2) of PD pulses. The associated PRPD sub-patterns, PD pulses relevant to each centroid and the corresponding time-frequency representations derived from APC separation are shown in Figure 14. As can be seen from Figure 14a and 14b, the PRPD sub-pattern relevant to group #1 is dominated by pulses with low amplitude while the PRPD sub-pattern relevant to group #2 mainly includes large-amplitude pulses. The phase range of group #1 basically locates in 0º-90º, 160º-270º and 340º-360º, which is quite similar with the PRPD sub-pattern relevant to Figure 11a. The similar phase property acquired from cavity defects was reported in reference [35] and [39]. Different with group #1, group #2 mostly concentrates in 0º-90º and 180º-270º which exhibits a typical surface discharge characteristic [40]. Accordingly, the visual inspections provide PD sources information coinciding with the real defects, which presents a preliminary validation of the proposed separation algorithm. It can be observed from Figure 14c and 14e that PD pulses of group #1 exhibit small amplitudes connected with short duration time and irregular waveshape, and the time-frequency representations are comparatively messy. The intrinsic small amplitude and measurement in a wide amplitude range result in the apparent mixture of the PD pulses with white noises, which is the main cause of irregular waveshape of group #1. Figure 14d and 14f display the centroid’s waveshape and time-frequency representation relevant to group #2, which presents large-amplitude pulses associated with longer duration time and smoother waveshape. Besides, the time-frequency representation of PD pulse of group #2 is simpler than that of group #1.

Frequency(MHz)


5.0E+3

30 20 10 0

2.5E+3 Time(ns)

5.0E+3

(f)


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reported in Figure 17a and 17b. It can be found that the complex PRPD pattern is clearly divided into two distinctive PRPD sub-patterns. Group #1 is dominated by small-amplitude pulses that mainly locate in 0º-90º, 160º-270º and 340º-360º and has been identified as a typical property of internal PD in previous investigations while group #2 is mostly characterized by obviously asymmetric pattern with large amplitudes and reported to be similar to the PRPD pattern of oil corona in reference [37]. Figures 17c and 17d give the PD pulses of each centroid relevant to group #1 and group #2, and the corresponding time-frequency representations are provided in Figures 17e and 17f. As can be observed in Figure 17d, the signal shape with 5 μs seems insufficient to collect the waveshape of group #2. For further investigations, we acquire the pulse from original oscilloscope data with a comparatively large width of 30 μs, shown in Figure 18a. It is easily identified that the pulses relevant to group #2 has larger amplitude and pulse width than that of group #1. Besides, there is an obvious high-frequency oscillation in the pulse’s rise (see in Figure 18b), which is judged as a typical discharge characteristic of oil corona since the similar signal characteristics have been presented in [40] and [41], and shown in Figure 19. Hence, visual validation confirms the effectiveness of the proposed ST combined with APC algorithm for separation of internal of PD and oil corona.

(a)

(b)

Figure 18. A typical pulse waveshape of corona discharge in oil.

0.7398 0.7682 2.3119 2.4213 28.0000 28.0000 0.8957 0.6362 0.7308 0.6736 2.3369 2.3363 25.0000 29.0000 1.1277 0.7038 1.0099 0.7488 2.9374 2.0282 20.0000 29.0000 0.6551 0.5873 2.0056 9.7880 43.0000


(a)

0.1479 -0.1813 2.3980 2.0808 25.0000 11.0000 0.1777 0.6517 0.1136 -0.0819 2.2495 2.0453 22.0000 11.0000 0.2215 0.5352 -0.0608 -0.5184 3.5296 2.5137 19.0000 7.0000 0.0490 0.4604 -0.0383 2.1672 59.0000


(b)

Figure 20. Statistical features of the pulses relevant to each group of Figure 17: (a) is group #1, (b) is group #2, and identified as G and C, respectively.

For automatic validation, 27 statistical features are then calculated based on the separated PRPD sub-patterns, listed in Figure 20, to achieve the identification of defect information. Input the extracted features into the PSO-SVM classifier, the relevant PRPD sub-patterns are determined as G and C type, respectively, which coincide with the real experimental defect models and verify the effectiveness of the proposed separation algorithm. 5.4 SEPARATION OF PD DUE TO AIR/OIL INTERFACE DISCHARGE AND SURFACE DISCHARGE IN OIL The last case associated with two simultaneous PD sources reported here consists of surface discharge in oil and air/oil interface discharge. It has been reported that air/oil interface may be generated by incomplete filling of transformer tanks [35], and PD would occur in the interface when the afforded voltage is over the inception voltage. Figure 21 gives the acquired PRPD pattern when the voltage applied on the two defects is 24 kV and N = 3343 pulses are obtained.

(a)

Figure 21. PRPD pattern of the mixed defect models consisting of S and I. (b) Figure 19. Pulse waveshapes of oil corona in references: (a) [40] and (b) [41].

However, it needs further in-depth investigations on the pulse acquisition technique, when the proposed ST combined with APC algorithm is used for diagnosis of oil-paper insulation system, due to the fact that the complete pulse information are not provided although a clear separation result is obtained in this case.

The next step still lies in the computation of time-frequency similarity matrix S and preference boundaries pmax and pmin. In this example, we obtain pmax = 0.99 and pmin = -609. Then, p(i) = -100 is adopted to implement APC. Two groups of pulses labeled as #1 and #2 are obtained. The derived PRPD sub-patterns, pulse centroids and the corresponding time-frequency representations are shown in Figure 22.


(a) 20

-3 0

2.5E+3 Time(ns)

6 DISCUSSION

-10 -20 0

5.0E+3

2.5E+3 Time(ns)

(c)

(d) 50

40

40

Frequency(MHz)

50

30 20 10 0

2.5E+3 Time(ns)

5.0E+3

5.0E+3

30 20 10 0

2.5E+3 Time(ns)

(e)

5.0E+3

(f)


It can be easily found from Figure 22a and 22b that the PRPD sub-patterns relevant to group #1 and #2 are visibly different. Group #1 presents the phase distribution of 0º-90º and 180º-270º with relatively small-amplitude pulses while a phase range of 45º-100º and 200º-270º with large-amplitude pulses in group #2 are obtained. The phase distribution properties of group #1 and group #2 are quite similar with that of surface PD in oil and air/oil interface PD reported in [35]. With regard to the pulse 0.3451 0.1167 2.1577 2.6779 30.0000 27.0000 1.1467 0.7478 0.2132 0.2495 1.8852 2.5081 28.0000 26.0000 0.9593 0.6721 0.0168 0.1584 2.2162 2.5322 20.0000 28.0000 1.4248 0.6492 1.4367 5.6212 60.0000


(a)

0.4310 -0.1012 2.6539 2.6026 17.0000 18.0000 0.9146 0.6357 0.2505 0.0490 2.4211 2.1961 18.0000 19.0000 1.2475 0.4136 0.4763 0.2018 2.8788 3.3039 18.0000 17.0000 0.4268 0.4561 1.1635 4.4169 53.0000


(b)

Figure 23. Statistical features of the pulses relevant to each group of Figure 22: (a) is group #1, (b) is group #2, and identified as S and I, respectively.

6.1 THE ADVANTAGES OF STA-BASED SIMILARITY OVER PULSE-BASED SIMILARITY The above results illustrate that the proposed separation and classification algorithms are capable of eliminating pulse-shaped noises and achieving separation of two simultaneous PD sources. It has been reported that noises suppression or PD sources separation problems mainly resort to data processing for pulse waveshapes [10]. In this work, ST is employed to concentrate the time-domain and frequency-domain information into the time-frequency plane. The most important advantage of using STA-based time-frequency representations instead of original PD pulses can be well illustrated in Figure 24 when the polarity of PD pulses is taken into account. 1

1

Normalized Amplitude

-2

0

0.5 0 -0.5 -1 0

2.5E+3 Time(ns)

0.5 0 -0.5 -1 0

5.0E+3

(a) 50

40

40

30 20 10 0

2.5E+3 Time(ns)

(c)

2.5E+3 Time(ns)

5.0E+3

(b)

50 Frequency(MHz)

0 -1

10

Normalised Amplitude

1

Frequency(MHz)

Amplitude(V)

Amplitude(V)

2

1051

waveshapes, group #1 and #2 look a little similar, with the connection that pulse of group #1 attenuates faster than group #2. Moreover, as can be seen from Figure 22e and 22f, clear differences of the time-frequency representations are confirmed between group #1 and #2. Specifically, the major energy of group #1 pulses locates at 10-25 MHz combined with less energy converges at 5 MHz, while pulses relevant to group #2 largely concentrates on 5 MHz. Furthermore, 27 statistical features of PRPD sub-patterns relevant to group #1 and group #2 are extracted and shown in Figure 23. The identification results of group #1 and #2 by PSO-SVM classifier are S and I, which clearly validate the effectiveness of the proposed separation algorithm.

(b)

3

Frequency(MHz)

Vol. 22, No. 2; April 2015

5.0E+3

30 20 10 0

2.5E+3 Time(ns)

5.0E+3

(d)

Figure 24. Examples of PD pulses of positive and negative cycles and their time-frequency representations: (a) and (c) is of positive cycle, (b) and (d) is of negative cycle.

Figure 24a and 24b are two typical waveshapes of PD pulses after normalization relevant to group #1 in Figure 22, which occur in the positive and negative cycle, respectively. The pulse normalization is achieved by equation (3). Obvious differences between positive and negative PD pulses are clearly verified. There are two simple ways to calculate the pulse-based similarity between the normalized pulses: (1) use the absolute value of

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each pulse expressed as equation (5), and (2) revert the negative pulse expressed as equation (6).

  x (k )  X   x (k )  X  m

S  i, j  

i

k 1

i

j

j

m 2   m    xi (k )  X i    x j (k )  X j  k 1  k 1



m

S  i, j  

  x (k )  x   x k 1

i

i

j



2

  

 x j (k ) 

m 2  m 2     xi (k )  xi     x j  x j (k )    k 1  k 1 

(5)

(6)

where xi and xj represent the positive and negative PD pulses, Xi and Xj are the mean value of absolute pulses xi (k ) and xi (k ) ,

exemplar as the same [27]. The second one is to assume all the p(i) by the weighted median of S [42]. Finally, adaptive determination of p(i) is incorporated into APC computation to make sure the obtained clustering results are optimal [43]. However, how to define the object function is still a problem for p(i) assignment for the strategy. Besides, the optimization of p(i) would add the computational time of APC to a large extent. Therefore, the third method is still under evaluation for practical applications. For PD pulses separation, we also try to resort to the second p(i) assignment strategy to perform the examples testing. Unfortunately, significantly over-separation of PD pulses is obtained, namely, APC generates a lot of sub-groups of pulses. It brings large challenges to the relevant sub-patterns identification. When the first p(i) assignment strategy is used, employing p(i) = -100 satisfactory results are obtained for the separation of two PD sources simultaneously active.

S  i, j  

k 1

i

i

j

j

m 2  m 2     xi (k )  xi     x j  x j (k )    k 1  k 1 

cluster number

p(i)

(b)

cluster number

m

  x (k )  x   x (k )  x 

p(i)

(a)

cluster number

The pulse-based similarities between two PD pulses using the above two methods are 0.77 and 0.8, respectively. Figure 24c and 24d give the corresponding ST time-frequency representations of the two pulses, which produce a similarity of 0.95. Accordingly, ST provides larger similarities between the pulses belonging to the same PD sources than original pulses. Another comparison can be performed using pulses in positive cycle belonging to different PD sources. The similarity between the two pulses in Figure 22c and 22d is determined as 0.49 using equation (7) while the relevant time-frequency similarity is 0.5, which is practically unchanged.

cluster number

xi and x j are the mean value of xi and xj.

(7)

To sum up, the primary advantage of using ST instead of original PD pulse lies in that ST can not only significantly increase the similarities between pulses belonging to the same PD sources but also almost keep the similarities between pulses belonging to different PD sources unchanged, namely, ST is more appropriate for generating the similarity matrix S to facilitate the APC-based PD pulses separation. 6.2 THE INFLUENCE OF APC PREFERENCES The preference p is a quite important parameter, which determines the generated cluster number. Figure 25 reports the generated cluster number with the increasing of p(i) based on the acquired data of two simultaneous PD sources described in Section 5.1 to 5.4. The numerical ranges of p(i) in Figure 25 are all from pmin to -1. It can be clearly found that the cluster number of APC are often less than 3 when APC preference p(i) is less than -50 under all the circumstances. Rapid increase of the cluster number with the development of p(i) larger than -50 are obtained. Hence, application of the proposed method for real-time PD sources separation needs the pre-determination of appropriate preference p(i). In practical applications, three methods are often used to produce p. The first one is the method used in this work, which regards the probability of each sample to be the potential

p(i)

(c)

p(i)

(d)

Figure 25. The influences of APC preferences on the generated cluster number for different validation examples: (a) pulse-shaped noises rejection, (b) separation of internal PD and surface PD in oil, (c) separation of internal PD and corona in oil, (d) separation of PD due to air/oil interface and surface PD in oil.

6.3 EFFICIENT COMPUTATION OF SIMILARITY MATRIX BETWEEN PD PULSES Similarity matrix S of different pulses is the input parameter of APC, which directly determines the PD pulses separation performances. Since a large number usually thousands of pulses are often acquired to achieve statistical pattern analysis, computation of S seems to be a huge task. As an example, N = 3000 PD pulses are obtained for separation, the dimension of S should be 3000×3000. In this work, all the algorithms are computationally achieved by MATLAB R2009b. The computer hardware includes a 2.93 GHz dual-core processor and 4 GB RAM. For efficient computation of S, a “matrix-to-vector” transformation based calculation procedure is introduced. When PD pulses with 5μs width are acquired, and dimension of ST time-frequency representations Aj is 250×500. In the proposed efficient method, the time-frequency representations Aj are first transformed to column vectors vj. Then, all the time-frequency vectors are assembled to a matrix V in which each column represents a transformed time-frequency representation. Finally, a MATLAB function “corrcoef” is employed based on V to

Vol. 22, No. 2; April 2015

Table 5. Computational time of time-frequency similarity matrix S in the computer with high configuration. Pulses number 1000 2000 3000 4000 5000 6000 S/s 4 13 25 44 68 95 Table 6. Computational time of APC for partial discharge pulses separation. Test examples G+N G+S G+C S+I APC / s 32 212 127 138

Table 6 reports the APC computational time relevant to the four examples detailed in Section 5.1 to Section 5.4, which are expressed as G+N, G+S, G+C and S+I, respectively. APC is an iterative optimization algorithm in which the computational time is largely influenced by the data separability. It can be found from Table 6 that obvious differences of the computational time relevant to four examples are obtained. Except G+N, it all needs 3-4 minutes to accomplish the PD pulses separation. Similar with similarity matrix S, the computational time of APC are all less than 1 minute using the high-configuration computer. Besides, several methods have been proposed to improve the computational efficiency of APC [44-46], which will facilitate the real time applications of the proposed separation algorithm. 6.4 COMPARISONS WITH METHODS FROM THE STATE OF THE ART Two representative PD separation methods from the state of the art including NACF [4, 11] and ETFA [6-10] are used to carry out comparisons with the proposed hybrid separation

6.4.1 COMPARISONS BASED ON PULSE CURRENT DETECTOR

Figure 26 reports typical normalized pulses of surface PD in oil and PD due to air/oil interface discharge under 22kV. 1000 PD pulses of each PD source are acquired with 5μs width, and all the 2000 pulses are then separated by NACF+FCM, ETFA+FCM and ST+APC, respectively. The obtained NACF relevant to PD pulses in Figure 26 are given in Figure 27. It can be easily seen that NACF data provide a larger differences of two PD sources rather than the recorded PD pulses. Thus, 2000 NACF data are directly used as input vectors of FCM to obtain the separation results. 1

1


To solve the problem that S consumes comparatively much time, we further calculate S resort to a high-configuration computer including 3.4 GHz eight-core processor and 16 GB RAM instead of the above normal computer, and the computational time with different number of pulses are detailed in Table 5. As can be seen, resorting to a high-configuration computer, the computation of S only needs 2 fewer minutes when N = 6000 pulses are recorded. As a consequence, using the proposed method for PD pulses separation needs a computer with high-configuration for practical applications when a large number of pulses are acquired.

0.5 0 -0.5 -1 0

2.5E+3 Time(ns)

0.5 0 -0.5 -1 0

5.0E+3

2.5E+3 Time(ns)

(a)

5.0E+3

(b)

Figure 26. Typical normalized PD pulses of two PD sources: (a) surface PD in oil, (b) PD due to interface discharge. 1

1

0.8

0.8

0.6

0.6 NACF

Table 4. Computational time of ST and time-frequency similarity matrix S. Pulses number 1000 2000 3000 4000 5000 6000 ST / s 7 16 22 30 36 47 S / min 0.2 6 15 29 42 51

algorithm based on ST and APC. When NACF and ETFA are employed for PD pulses separation, according to [4-9], additional clustering tools should be used. In this section, a classic fuzzy C-means clustering (FCM) is adopted to obtain the PD separation results. The above methods are shortly expressed as NACF+FCM and ETFA+FCM, respectively. The computational details of NACF, ETFA and FCM successful separation rate were respectively given in [4], [7] and [47]. The comparisons of NACF+FCM, ETFA+FCM and ST+APC are investigated by two examples of two PD sources separation relevant to different measurement setup. The first example is based on pulse current detector that is the same with previous investigations, and the second one is based on ultra-high-frequency (UHF) antenna that is another typical detector for PD measurement. The PD pulses associated with two PD sources are recorded by each single source and artificially combined together, and thereby the true sources of each PD pulse are known before separation. In this way, all the separation performances of NACF+FCM, ETFA+FCM and ST+APC are described by successful separate rate [47].


acquire the similarity matrix S. The similarities using the efficient method are confirmed to be the same with that between the time-frequency matrices. Table 4 shows the computational time of ST and similarity matrix S when different numbers of pulses are acquired. It could be found that ST has a very fast responding time less than 0.01 second to a PD pulse, which is very suitable for online PD monitoring applications. It can also be confirmed by the computational time of ST of N = 6000 pulses, which only needs less than 1minute. However, the computational time of S is shown to be comparatively large. When N = 2000, the computation of S needs more than 5 minutes. Besides, S calculation consumes nearly 1 hour with N = 6000. The evaluation results demonstrate that the computation of time-frequency similarity matrix S seems unsuitable for online PD pulses processing based on a normal computer.

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NACF


0.4

0.4

0.2

0.2

0 0

100

200

300

(a)

400

500

0 0

100

200

(b)

300

400

500

Figure 27. The relevant NACF of PD pulses in Figure 26: (a) surface PD in oil, (b) PD due to interface discharge.

Another method ETFA is also performed on the 2000 PD pulses. The equivalent time length σT and equivalent bandwidth σF of all pulses are computed and reported in Figure 28. The blue

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Ke Wang et al.: A Hybrid Algorithm Based on S Transform and Affinity Propagation Clustering for Separation Normalized Amplitude

points represent PD pulses of air/oil interface discharge while the red points indicate PD pulses of surface discharge in oil. The ETFA results show that clear different locations in time-frequency plane of the two PD sources are obtained.

1

0

-1

0

50

100 Time(ns)

150

200

¦Ò F (MHz)


(a) 1

0

-1

0

50

100 Time(ns)

150

200

(b)

Figure 30. Examples of normalized PD pulses recorded by UHF antenna: (a) surface discharge in oil, (b) corona discharge in oil. 1

0.8

0.8

0.6

0.6 NACF

FCM is further adopted to separate the two PD sources based on the obtained σT and σF data, and the separation results are reported in Figure 29. Although the pulses of two PD sources are visibly separated in time-frequency plane, the separation results of FCM are not so satisfactory.

1

NACF

¦Ò T (ms) Figure 28. The ETFA computation results of two PD sources reported in Figure 26.

0.4

0.4

0.2

0 0

0.2

200

400

(a)

600

800

1000

0 0

200

400

600

800

1000

(b)

Figure 31. The relevant NACF of UHF pulses in Figure 30: (a) surface discharge in oil, (b) corona discharge in oil.

Figure 29. FCM separation results of ETFA data of two PD sources in Figure 28.

The PD pulses are finally separated by the proposed ST+APC algorithm, in which p(i) = -100 are chosen to implement APC computation. The successful separation rates of NACF+FCM, ETFA+FCM and ST+APC are reported in Table 7. It can be obtained that the proposed ST+APC can achieve high separation performance as well as NACF+FCM, while ETFA+FCM only gives a successful separation rate of 84.6%. Table 7. Successful separation rates of two PD sources using different separation methods based on pulse current detector. Separation methods Successful separation rate NACF+FCM 98.5% ETFA+FCM 84.6% ST+APC 99.5%

6.4.2 COMPARISONS BASED ON UHF ANTENNA

Another comparison of NACF+FCM, ETFA+FCM and ST+APC is performed based on UHF detection arrangement. The experiment details can be found in [47]. In this section, 150 UHF pulses of surface in oil and 150 UHF pulses of corona in oil are used for algorithms testing. The typical UHF pulses of

surface discharge and corona discharge in oil are reported in Figure 30. The NACF and ETFA computation results are given in Figure 31 and Figure 32. FCM separation result of ETFA is given in Figure 33. Clear differences of NACF between the pulses of two PD sources and different locations in time-frequency plane of two PD sources are both confirmed. The separation result of ETFA+FCM is still not satisfactory according to Figure 33. ST+APC is further implemented to separate the UHF pulses of two PD sources with p(i) = -100. The successful separation rates of NACF+FCM, ETFA+FCM and ST+APC are presented in Table 8. Just like the separation results in Section 6.4.1, ST+APC and NACF+FCM achieve good separation results while ETFA+FCM reports a successful separation rate of 89%. Table 8. Successful separation rates of two PD sources using different separation methods based on UHF antenna. Separation methods Successful separation rates NACF+FCM 99% ETFA+FCM 89% ST+APC 99.67%

Based on the investigations in Section 6.4.1 and 6.4.2, the superiority of ST+APC for two PD sources separation can be confirmed as its good separation performances as well as NACF+FCM. The advantage of ETFA lies in the visual display of separation results which can be used to determine the number of PD sources. Application of ETFA to PD sources separation needs improvements on the clustering algorithm to obtain satisfactory separation results. A feasible method named modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for this issue has been reported in [11] when clusters partially overlapped are taken into account, and thereby


Vol. 22, No. 2; April 2015

1055

¦Ò F (MHz)

by the variation curve of APC cluster number versus p(i). Finally, APC with the chosen p(i) can give the separation results of PD pulses based on the time-frequency similarity matrix S. All the procedures are summarized in Figure 34.

¦Ò T (ns) Figure 32. The ETFA computation results of two PD sources reported in Figure 30. Figure 34. Flowchart of the proposed method with the assistance of ETFA for PD pulses separation.

Figure 33. FCM separation results of ETFA data of two PD sources in Figure 32.

most PD pulses belonging to each source can be separated at the cost of reducing a few overlapped pulses. 6.5 THE LIMITATIONS OF THE PROPOSED METHOD AND FUTURE WORKS From what we have discussed above, two major limitations of the proposed separation algorithm are derived. The first one is the computation efficiency of time-frequency similarity matrix S, and the second one is the determination of appropriate APC preference p(i). With rapid development of computer technology, efficient similarity matrix computation can be obtained by a high-configuration computer to make sure the processing time short enough for real time applications. In addition, improvements on the similarity computation may constitute another tool for this issue. The discussion in 6.4.3 implies potential real time processing capability of the proposed method for PD pulses separation. Therefore, future works will be concentrated on the determination of appropriate APC preference p(i) for PD sources separation. The superior visual display of ETFA for PD pulses may be a solution to this problem. When the time-frequency similarity matrix of PD pulses are obtained, the variation curve of the generated cluster number of APC versus p(i) can be derived, which is similar with Figure 25. The ETFA processing results of the PD pulses displayed in the time-frequency plane can give valuable references of the possible number of PD sources, which is more accurate for well-skilled engineers. Accordingly, appropriate APC preference p(i) relevant to the pre-determined cluster number can be automatically confirmed

ETFA assisted determination of p(i) still needs enough practical experiences for the engineers to judge the PD sources number based on ETFA computation results. An automatic identification method of appropriate APC preference p(i) can be derived from [4] where the number of PD sources is pre-determined by similarity comparisons of PD pulses. The method is under the premise that only classes containing a large number of pulses more than 100 are effectively identified based on statistical analysis of pulses [4, 48-49]. In the obtained time-frequency similarity matrix S, there is a one-to-one correspondence between the rows or columns and the PD pulses. Each row or column represents the similarities between a specific PD pulse and all PD pulses. The derived method is based on the similarity matrix S to determine appropriate APC preference p(i), which is outlined as follows: Step 1. Step 2.

Step 3. Step 4. Step 5. Step 6.

Calculate the time-frequency similarity matrix S of PD pulses using equation (4). Set a similarity threshold ρ, and select the pulses with similarity larger than ρ in the first column of S and assign them as the same class. The similarities between other pulses are used to form a new similarity matrix S1. Select the pulses larger than ρ in the first column of S1 and assign them as another class. Repeat step3 and step 4 until all pulses are assigned. Choose the number of classes including pulses more than 100 as the PD sources number, and the appropriate preference p(i) can be determined.

The above procedures can be automatically implemented with the pre-defined similarity threshold ρ. The above mentioned method can be further used to achieve the separation of more than two PD sources without any human assistance. However, it should be mentioned that the determination of similarity threshold ρ also needs in-depth investigations both by laboratory and field testing. In future works, experiments on more than two PD sources in laboratory are adopted to determine the similarity threshold ρ. Besides, the selected similarity threshold ρ is also employed for on-field PD data analysis based ST combined APC algorithm, and more evidences will be provided.

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7 CONCLUSION A new hybrid algorithm for separation of two simultaneous PD sources of oil-paper insulation using ST and APC is presented in this paper. Artificial defect models of oil-paper insulation are created for simulation of different two simultaneous PD sources and algorithm testing. The conclusions are summarized as follows: (1) A time-frequency analysis method based on ST is developed for PD pulses, which can collect the time-domain and frequency-domain information simultaneously, and meanwhile eliminate the polarity influence of PD pulses. Performances evaluation results demonstrate that ST is very suitable for PD online applications. (2) A PD pulses separation algorithm using time-frequency similarity based APC is adopted to separate the experimental pulses of two simultaneous PD sources. Applying the same p(i) for each PD pulse APC divides the original PD pulses into two specific groups in which each group of pulses have similar time-frequency characteristics. (3) A SVM-based classification model is designed for separation results validation. The identification results of the two PRPD sub-patterns derived from ST combined with APC based on the classification model are used for separation validation. The separation results of PD data produced by artificial defect models in laboratory demonstrate that ST combined with APC can effectively eliminate pulse-shaped noises and separate pulses of two simultaneous PD sources. (4) The computational time of time-frequency similarity matrix S dramatically increases with the addition of PD pulse number. A high-configuration computer is needed for practical applications of ST combined with APC. Furthermore, how to determine the appropriate APC preference p is the most important focus in future work, which would provide solid foundations for on-site PD diagnosis.

APPENDIX A.1 S TRANSFORM ST is an indispensable time-frequency spectral localization technique that combines the individual advantages of short-time Fourier transform (STFT) and continuous wavelet transform (CWT). The expression of ST is given as 

S  , f    x(t ) w  t   , f  dt 

(A1)

where x(t) represents the original signal, and τ is a parameter that controls the location in the timeline. The mother “wavelet” is defined as

f

w(t , f ) 

2

e



t2 f 2 2

e  j 2 ft

(A2)

Thus, ST can be explicitly written as 

f



2

S  , f    x(t )

e



( t  )2 f 2 2

e j 2 ft dt

(A3)

and the width of the Gaussian window is

1  f

ST can also be expressed as an operation of the Fourier spectrum X(f) of x(t) 

S  , f    X (  f )e



2 2 2 f2



e j 2 d , f ≠ 0

Conversely, the Fourier spectrum can be directly obtained by averaging the local spectra as 

X ( f )   S  , f d

(A6)



The original pulse x(t) can also be restored using an inverse ST

x(t )  







 

S  , f e j 2 ft d df

(A7)

Usually, the signals x(t) are all recorded in discrete form, which can be expressed as x(kT), where T is the time sampling interval, and N is the total sampling number, k = 0, 1, …, N-1. From equation (A5), ST can be computed quickly by FT. Then the discrete ST can be given by letting τ → iT and f → n/NT as n    S  iT , NT  

N 1



X[

m0

S iT ,0 

mn  ]e NT

2 2 m 2 n2

e

j 2  mk N

1 N 1  m   x  N m0  NT 

n asd T

n = 0

(A8) (A9)

where n, i, m = 0, 1, …, N-1, and X[n/NT] is the discrete FT of x(kT), which can be expressed as  n  1 X   NT  N

N 1

 x(kT )e



j 2 nk N

(A10)

k 0

Accordingly, the discrete ST can be quickly computed by taking advantage of FFT’s efficiency. The output of ST is a k×n matrix whose rows pertain to frequency and columns represent time. The ST-amplitude (STA) matrix, which is often employed for practical applications, is obtained as

n   A  kT , f   S  kT , NT  

(A11)

A.2 THE COMPUTATION PROCEDURES OF AFFINITY PROPAGATION CLUSTERING APC uses two evidences “responsibility” and “availability” for message-passing iterations to achieve optimal centroids and the corresponding data samples belonging to each cluster. The responsibility r(i, k) implies how well-suited point k is to serve as the exemplar for point i. The availability a(i, k) is used to represent how appropriate the point i would choose point k as its centroid. Based on the above two evidences, the computation procedures of APC are outlined as follows: Step 1. Calculate the similarity matrix S=[S(i, j)]N×N. Step 2. Input p and λ, and initialize r(i, k) = a(i, k) = 0 for all i, k. Step 3. Set S(i, i) = p(i). Step 4. Responsibility r(i, k) updates: r old (i , k )  r (i , k )

(A4)

(A5)

r (i , k )  S ( i , k )  max  a ( i , j )  S ( i , j )  j: j  k

(A12) (A13)


Vol. 22, No. 2; April 2015

r new (i , k )  (1   ) r (i , k )   r old (i , k )

(A14)

r (i , k )  r new (i , k )

(A15)

Step 5. Availability a(i, k) updates:

(A16)

a old (i , k )  a (i , k ) a (k , k ) 

 max{0, r ( j , k )}

(A17)

j: j  k

  a (i, k )  min 0,  r ( k , k )   

  max{0, r ( j , k )}  j : j{i , k }  



a new (i , k )  (1   ) a (i , k )   a old (i , k )

(A18) (A19) (A20)

a (i , k )  a new (i , k )

In equations (A12)-(A20), r old (i , k ) and a old (i , k ) are variables of the last iteration, r new (i, k ) and a new (i, k ) represent variables of the current iteration, r (i, k ) and a(i, k ) are intermediate variables during the iterations. Step 6. Repeat step 4 and step 5 until a high quality set of exemplars and the corresponding clusters are obtained, which means the iteration reaches maximum T or centroids have no change after l-step iterations (l 0 is the penalty factor, and ξi is the slack variable which indicates the classification errors for the training dataset. Finally, binary SVM classifier can be expressed by a decision function:

t 1 ij

REFERENCES

(A27)

subject to  yi  w  xi  b   1   i    i  0

The authors would like to thank the fund for innovative research groups of China (51021005) and national natural science foundation of China (51277187) for the financial support provided. The authors also wish to thank Dr. C. Guo for his kind supports in the experiments and the anonymous reviewers for their valuable comments.

t ij

[7] [8]

[9]

[10] [11]

(A30) [12]

(A31)

where t = 1, 2, …, Tmax denotes the ith iteration, i = 1, 2, …., m, j = 1, 2, …, D, w is inertial weight that represents the influence of previous velocity on current velocity, c1 and c2 are non-negative learning factors, r1 and r2 are two random number located in [0, 1], vi = (vi1, vi2, …, viD)T represents the velocity of the ith particle, pi = (pi1, pi2, …, piD)T is the ith particle’s optimal position, pg = (pg1, pg2, …., pgD)T is global optimal position of the swarm.

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Ke Wang was born in Anhui, China in 1987. He received the Bachelor and Ph.D. degrees in electrical engineering in 2008 and 2013, respectively from Chongqing University, Chongqing, China. From 2008 to 2010, he was a M.S. degree candidate in Electrical Engineering School at Chongqing University. In 2013, He joined China Electric Power Research Institute where he is engineer of Transformer Technology Laboratory, Department of High Voltage. His major research interests include high voltage insulation technologies, transformer related technologies, aging mechanism of transformer insulation, aging evaluation and fault diagnosis of insulation systems in high voltage equipment, partial discharges, high-frequency signal processing, image processing, machine learning, and pattern recognition. He is author/ coauthor of over 20 journal and international conferences. Jinzhong Li was born in Shanxi, China in 1974. He received the Bachelor and M. S. degrees in electrical engineering in 1997 and 2006, respectively from Xi’an Jiaotong University, Xi’an, China and North China Electric Power University, Beijing, China. In 1997, he joined Shanxi Electric Power Research Institute. In 2006, He joined China Electric Power Research Institute, where he is senior engineer and Director of Transformer Technology Laboratory, Department of High Voltage. He is the contact of IEC TC115 and TC38 as well as member of working group of CIGRE B3.29, A3 and IEEE P1861. His main research interests are high voltage insulation technologies and transformer related technologies. Shuqi Zhang was born in Liaoning, China in 1981. He received the Bachelor and M. S. degrees in electrical engineering in 2005 and 2007, respectively from North China Electric Power University, Beijing, China. In 2007, He joined China Electric Power Research Institute, where he is senior engineer and Associated Director of Transformer Technology Laboratory, Department of High Voltage. His main research interests are high voltage insulation technologies and transformer related technologies.

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Ruijin Liao was born in Sichuan, China in 1963. He received the M.S. and Ph.D. degrees in electrical engineering from Xi’an Jiaotong University, Xi’an, China and Chongqing University, Chongqing, China, respectively. Since 1999 he is a professor of Electrical Engineering School at Chongqing University, China. His research activities lie in the field of on-line monitoring of insulation condition and fault diagnosis for high voltage apparatus, space charge measurement of high voltage transmission line, mechanism and characteristic of corona discharge in air, modification of Kraft paper and mineral oil for transformers, anti-icing technology of outdoor insulator, as well as ageing mechanism and diagnosis for power transformer. He is author/ coauthor of three books and over 130 journal and international conferences.

Feifei Wu was born in Zhejiang, China in 1985. He received the Bachelor degree and Ph.D. degrees in electrical engineering in 2009 and 2014, respectively Chongqing University, Chongqing, China. From 2009 to 2010, he was a M.S. degree candidate in Electrical Engineering School at Chongqing University. His major research interests include space charge measurement technology in high voltage transmission line, mechanism and characteristic of corona discharge. He is now working at Chengdu Power Supply Company.

Lijun Yang was born in Sichuan, China in 1980. She received her M.S. and Ph.D. degrees in electrical engineering from Chongqing University, China in 2004 and 2009. She is now a professor of Electrical Engineering School at Chongqing University. Her major research interests include online detection of insulation condition of electrical devices, partial discharges, and insulation fault diagnosis for high voltage equipment. She is author/ coauthor of over 50 journal and international conferences.

Jian Li (M’05-SM’11) received the M.S. and Ph.D. degrees in electrical engineering in 1997 and 2001, respectively from Chongqing University, Chongqing, China. He is currently a professor and the head of High Voltage and Insulation Technology Department at Chongqing University. His major research interests include online detection of insulation condition in electrical devices, partial discharges, and insulation fault diagnosis for high voltage equipment, environment-friendly insulating liquid, anti-icing coating of insulator and transmission line, and polyethylene/organic-montmorillonite nano-composites. He is an author and coauthor of more than 50 journal papers and 50 papers published in proceedings of international conferences. Stanislaw Grzybowski (SM’70-F’99-LF’2002) was born in 1933, Poland. He received the M.Sc. and Ph.D. degrees in electrical engineering from the Technical University of Warsaw, in 1956 and 1964, respectively. In 1984, he obtained the Dr. Hab. (Dr. Habilitated) degree from the Technical University of Wroclaw, Poland. In 1956, he joined the faculty of Electrical Engineering at the Technical University of Poznan, Poland. In 1987, He joined Mississippi State University, where he is Professor and Director of High Voltage Laboratory in the Electrical and Computer Engineering Department. His main research interests are in the areas of high voltage engineering: electrical strength of high voltage devices, lightning protection of power systems, ships, aerostats, and other objects. He is author/co-author of three books in high voltage engineering, three problems books, and over 260 papers published in IEEE Transactions and in Proceedings of International and National Conferences. Jiaming Yan was born in Anhui, China in 1977. He received his M.S. and Ph.D. degrees in electrical engineering from Xihua University, China in 2006 and Chongqing University, China in 2011, respectively. Now, he is a lecturer in School of Electric Power Engineering of China University of Mining and Technology. His research area is online monitoring and fault diagnosis for power transformer.