Locating Pd in Transformers through Detailed Model and Neural Networks

Open access

Abstract

In a power transformer as one of the major component in electric power networks, partial discharge (PD) is a major source of insulation failure. Therefore the accurate and high speed techniques for locating of PD sources are required regarding to repair and maintenance. In this paper an attempt has been made to introduce the novel methods based on two different artificial neural networks (ANN) for identifying PD location in the power transformers. In present report Fuzzy ARTmap and Bayesian neural networks are employed for PD locating while using detailed model (DM) for a power transformer for simulation purposes. In present paper PD phenomenon is implemented in different points of transformer winding using threecapacitor model. Then impulse test is applied to transformer terminals in order to use produced current in neutral point for training and test of employed ANNs. In practice obtained current signals include noise components. Thus the performance of Fuzzy ARTmap and Bayesian networks for correct identification of PD location in a noisy condition for detected currents is also investigated. In this paper RBF learning procedure is used for Bayesian network, while Markov chain Monte Carlo (MCMC) method is employed for training of Fuzzy ARTmap network for locating PD in a power transformer winding and results are compared.

[1] NADERI, MOHAMMAD S.-VAKILIAN, M.-BLACKBURN, T. R.-PHUNG, B. T.-NADERI, MEHDI S.-NASIRI, A. : A Hybrid Transformer Model for Determination of Partial Discharge Location in Transformer Winding, IEEE Transactions on Dielectrics and Electrical Insulation 14, No. 2 (Apr 2007).

[2] MOORE, P. J.-PORTUGUES, I. E.-GLOVER, I. A. : Partial Discharge Investigation of a Power Transformer using Wireless Wideband Radio-Frequency Measurements, IEEE Transaction on Power Delivery 21 No. 1 (Jan 2006).

[3] WANG, Z. D.-CROSSLEY, P. A.-CORNICK, K. J.-ZHU, D. H. : An Algorithm for Partial Discharge Location in distribution Power Transformers, Power Engineering Society Winter Meeting, vol. 3, 23-27 Jan 2000, pp. 2217-2222.

[4] NAFISI, H.-DAVARI, M.-GHAREHPETIAN, G. B.-ABEDI, M. : Using Fuzzy ARTmap Neural Network for Determination of Partial Discharge Location in Power Transformers, in Proc. of IEEE, Power Tech, 2009.

[5] NAFAR, M.-ABEDI, M.-GHAREHPETIAN, G. B.-TAGHIPOUR, S.-YOUSEFPOUR, B. : Locating Partial Discharge in Transformer by Wavelet, WSEAS Transactions on circuits and systems 3 No. 6 (Aug 2004), 1499-1503.

[6] AKBARI, A.-WERLE, P.-BORSI, H.-GOCKENBACH, E. : Transfer Function-based Partial Discharge Localization in Power Transformers: a Feasibility Study, IEEE Electrical Insulation Magazine 18 No. 5 (Sep/Oct 2002), 22-32.

[7] SMITH, K. N.-PEREZ, R. A. : Locating Partial Discharges in a Power Generating System using Neural Networks and Wavelets, Annual Report Conference on Electrical Insulation and Dielectric Phenomena, 2002, pp. 458-461.

[8] DAVARI, M. ALE-EMRAN, S. M.-MOBARHANI, A. R.- NAFISI, H.-SALABEIGI, I.-GHAREHPETIAN, G. B. : A Novel Approach to VFTO Analysis of Power Transformers Including FVL based on Detailed Model, In Proceeding of IEEE International Conference on Industrial Technology (ICIT), Australia, February, 10-13, 2009.

[9] CARPENTER, G. A.-GROSSBERG, S. : ART2 : Self-Organization of Stable Category Recognition Codes for Analog Input Patterns, Applied Optics 26(23) (1987), 4919-4930.

[10] CARPENTER, G. A.-GROSSBERG, S. : ART3 : Hierarchical Search using Chemical Transmitters in Self-Organizing Pattern Recognition Architectures, Neural Networks (Publication) 3 (1990), 129-152.

[11] CARPENTER, G. A.-GROSSBERG, S.-REYNOLDS, J. H. : ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network, Neural Networks (Publication) 4 (1991), 565-588.

[12] CARPENTER, G. A.-GROSSBERG, S.-ROSEN, D. B. : Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System, Neural Networks (Publication) 4 (1991), 759-771.

[13] CARPENTER, G. A.-GROSSBERG, S.-MARKUZON, N.- REYNOLDS, J. H.-ROSEN, D. B. : Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps, IEEE Transactions on Neural Networks 3 (1992), 698-713.

[14] HAMED NAFISI-ABDI, B.-AGHAKHANI, A. : Comparison of Bayesian and Fuzzy ARTmap Networks in HV Transmission Lines Fault Diagnosis, In Proceeding of WSEAS International Conference on Selected Topics in Mathematical Methods and Computational Techniques in Electrical Engineering (MMACTEE ’10), Politehnica University of Timisoara, Romania, Oct 21-23, 2010.

[15] CHEN-FU CHIEN-SHI-LIN CHEN-YIH-SHIN LIN: Using Bayesian Network for Fault Location on Distribution Feeder, IEEE Transactions on Power Delivery 17 No. 13 (July 2002).

[16] SZOLOVITS, P.-PAUKER, S. : Categorical and Probabilistic Reasoning in Medical Diagnosis, Artif. Intell. 1 (1978), 115-144.

[17] SOVARONG, L.-COSTAS, J. S. : A General Equipment Diagnostic System and its Application on Photolithographic Sequences jour IEEE Trans. Semi-conductor Manufact..

[18] DUDA, R.-GASCHNING, J.-HART, P. : Model Design in the Prospector Consultant System for Mineral Exploration, in Expert Systems in the Microelectronic Age (D. Michie, ed.), Edinburgh Univ. Press, Edinburgh, U.K., 1979, pp. 153-167.

[19] BUNTINE, W. : A Guide to the Literature on Learning Probabilistic Networks from Data, IEEE Trans. Knowl. Data Eng. 8 No. 2 (1996), 195-210.

[20] WEI ZHAO-XIAOMIN BAI-JIAN DING-ZHU FANG- ZAIHUA LI-ZIGUAN ZHOU: A New Uncertain Fault Diagnosis Approach of Power System based on Markov Chain Monte Carlo Method, IEEE International Conference on Power System Technology, 2006, 1-4244-0111.

[21] DENISON-HOLMES-MALLICK-SMITH: Bayesian Methods for Nonlinear Classification and Regression, Wiley, 2002.

Journal of Electrical Engineering

The Journal of Slovak University of Technology

Journal Information


IMPACT FACTOR 2018: 0.636
5-year IMPACT FACTOR: 0.663

CiteScore 2018: 0.88

SCImago Journal Rank (SJR) 2018: 0.200
Source Normalized Impact per Paper (SNIP) 2018: 0.771

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 169 94 10
PDF Downloads 76 52 11