Open Access Open Access  Restricted Access Subscription or Fee Access

A Fuzzy Diagnostic System for Incipient Transformer Faults Based on DGA of the Insulating Transformer Oils


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iree.v11i3.8453

Abstract


Some costly repairs are recited when faults occur in transformer, therefore,  an early detection of the transformer faults results in a reduction of the transformer maintenance and it maintains the continuous operation of the power networks. Nowadays, Dissolved Gas Analysis (DGA) is the most common diagnostic tool to determine the faults occurred in insulating transformer oils by analyzing the gas evolved from it. In this paper, the Fuzzy Logic Transformer Fault Diagnostic System (FLTFDS) is used as an application of the dissolved gas analysis to diagnose the dominant transformer fault generated  by  the electrical and thermal stresses. FLTFDS is constructed on the basis of three common criteria:the International Electro-technical Commission standard (IEC Code Standard), the Central Electric Generating Board (CEGB) Standard based on Rogers four ratios, and Finally Duval Triangle Method; these criteria are used to interpret the transformer faults using fuzzy logic. There are three main transformer diagnostic faults: thermal, arcing and partial discharge faults. The FLTFDS output is a dominant fault based on the output of the three criteria. The FLTFDS solves the conflict among the outputs of the three common criteria because such criteria, give different results for the same case study. Data samples (386 samples) are collected from the electrical utility laboratory in Egypt and credited literature. These data samples are used as an input to the three criteria and the outputs of the three criteria are an input to the Fuzzy-based decision making system (FDMS)in order to determine the dominant fault. The results demonstrate that the accuracy of FLTFDS output is greater than the accuracy of the three common criteria.
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords


Fuzzy Logic System; Faults; Transformers; DGA

Full Text:

PDF


References


D. Bhalla, R. K. Bansal, H. O. Gupta, Transformer Incipient Fault Diagnosis Based on DGA using Fuzzy Logic, India International Conference on POWER ELECTRONICS (IICPE), 28-30 Jan. 2011.
http://dx.doi.org/10.1109/iicpe.2011.5728109

E. Dornenburg, W. Strittmater, Monitoring Oil Cooling Transformers by Gas Analysis, Brown Boveri Review, vol. 61, pp. 238-274, 1974.

R. R. Rogers, IEEE and IEC Codes to Interpret Incipient Faults in Transformers, Using Gas in Oil Analysis,IEEE Trans. on Electrical Insulation, vol. 13, no. 5, 1978, pp. 349-354.
http://dx.doi.org/10.1109/tei.1978.298141

M. Duval, Dissolved Gas Analysis: It Can Save Your Transformer,IEEE Electrical Insulation Magazine, vol. 5, no. 6, 1989, pp. 22-27.
http://dx.doi.org/10.1109/57.44605

IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers, IEEE Standard C57.104-2008, Feb. 2009.
http://dx.doi.org/10.1109/ieeestd.2009.4776518

“Interpretation of the analysis of gases in transformers and other oil-filled electrical equipment in service,” IEC Publ. 60599, March 1999.
http://dx.doi.org/10.3403/00003118u

R. Hooshmand, M. Banejad, Fuzzy Logic Application in Fault Diagnosis of Transformers Using Dissolved Gases,Journal of Electrical Engineering & Technology, vol. 3, No. 3, 2008, pp. 293-299.
http://dx.doi.org/10.5370/jeet.2008.3.3.293

Y. Zhang, X, Ding, Y. Liu, P. J. Griffin, An Artificial Neural Network Approach to Transformer Fault Diagnosis, IEEE Transactions on Power Delivery, vol. 11, no 4, 1996, pp. 1836-1841.
http://dx.doi.org/10.1109/61.544265

J. L. Guardado, J. L. Naredo, P. Moreno,C. R. Fuerte, A Comparative Study of Neural Network Efficiency in Power Transformers Diagnosis Using Dissolved Gas Analysis, IEEE Transactions on Power Delivery, vol. 16, no 4, 2001, pp. 643-647.
http://dx.doi.org/10.1109/61.956751

Y. C. Huang, H. C. Sun, Dissolved Gas Analysis of Mineral Oil for Power Transformer Fault Diagnosis Using Fuzzy Logic,IEEE Transactions on Dielectrics and Electrical Insulation,vol. 20, No. 3, June 2013, pp. 974-981.
http://dx.doi.org/10.1109/tdei.2013.6518967

C. E. Lin, J. M. Ling, C. L. Huang, An Expert System for Transformer Fault Diagnosis Using Dissolved Gas Analysis, IEEE Transaction on Power Delivery, vol. 8, no 1, 1993, pp. 231-238.
http://dx.doi.org/10.1109/61.180341

W. Xu, D. Wang, Z. Zhou, H. Chen, Fault Diagnosis of Power Transformers: Application of Fuzzy Set Theory, Expert Systems and Artificial Neural Networks,IEE Proc.-Sci. Meas. Technology, Vol. 144, no 1, 1997, pp. 39-44.
http://dx.doi.org/10.1049/ip-smt:19970856

Y. C. Huang,H. T. Yang, C. L. Huang, Developing a New Transformer Fault Diagnosis System through Evolutionary Fuzzy Logic, IEEE Transactions on Power Delivery, vol. 12, no 2, 1997, pp. 761-767.
http://dx.doi.org/10.1109/61.584363

Z. Wang, Y. Liu, P. J. Griffin, A Combined ANN and Expert System Tool for Transformer Fault Diagnosis, IEEE Transactions on Power Delivery, vol. 13, no 4, 1998, pp. 1224-1229.
http://dx.doi.org/10.1109/61.714488

K. Tomsovic, M. Tapper, T. Ingvarsson, A Fuzzy Information Approach to Integrating Different Transformer Diagnostic Methods, IEEE Transactions on Power Delivery, vol. 8, no 3, 1993, pp. 1638-1646.
http://dx.doi.org/10.1109/61.252690

H. T. Yang, C. C. Liao, J. H. Chou, Fuzzy Learning Vector Quantization Networks for Power Transformer condition Assessment, IEEE Transaction on Dielectrics and Electrical Insulation, vol. 8, no 1, 2001, pp. 143-149.
http://dx.doi.org/10.1109/94.910437

Y.M.Kim, S.J. Lee, H.D.Seo, J.R. Jung, H.J. Yang, Development of Dissolved Gas Analysis (DGA) Expert System Using New Diagnostic Algorithm For Oil immersed Transformers, Journal of Electrical Engineering & Technology, vol. 3, No. 3, 2008, pp. 293-299.
http://dx.doi.org/10.1109/cmd.2012.6416455

W. Chenghao, W. Tang, and Q. Wu, Dissolved Gas Analysis Method Based on Novel Feature Prioritisation and Support Vector Machine, IET Electric Power Applications, vol. 8, Issue 8, 2014, pp. 320–328.
http://dx.doi.org/10.1049/iet-epa.2014.0085

S. Souahlia, K. Bacha, A. Chaari, SVM-Based Decision for Power Transformers Fault Diagnosis Using Rogers and Doemenburg Ratios DGA, 10th International Multi-Conference on SYSTEMS, SIGNALS&DEVICES (SSD), March 18-21, 2013,pp. 1-6, Hammamet, Tunisia.
http://dx.doi.org/10.1109/ssd.2013.6564073

D. A. Mansour, A New Graphical Technique for the Interpretation of Dissolved Gas Analysis in Power Transformers,2012 Annual Report Conference on ELECTRICAL INSULATION AND DIELECTRIC PHENOMENA (CEIDP), 14-17 Oct. 2012, pp.195 – 198.
http://dx.doi.org/10.1109/ceidp.2012.6378754

S. S. M. Ghoneim, I. B. M. Taha,"A new approach of DGA interpretation technique for transformer fault diagnosis ", International Journal of Electrical Power and Energy Systems, 81, 2016, pp. 265–274.
http://dx.doi.org/10.1016/j.ijepes.2016.02.018

S. S. M. Ghoneim, I. B. M. Taha, and N. I. Elkalashy," Integrated ANN-Based Proactive Fault Diagnostic Scheme for Power Transformers Using Dissolved Gas Analysis", IEEE Transactions on Dielectric and Electrical Insulation, Vol. 23, No. 3, 2016.

I. B. M. Taha, S. S. M. Ghoneim, H. G. Zaini, Refining DGA Methods of IEC Code and Rogers Four Ratios for Transformer Fault Diagnosis, 2016 IEEE General Meeting, 17-21 July 2016, Boston, USA.

H. Yang and C. C. Liao, Adaptive Fuzzy Diagnosis System for Dissolved Gas Analysis of Power Transformers, IEEE Transactions on Power Delivery, vol. 14, no 4, Oct. 1999, pp. 1342-1350.
http://dx.doi.org/10.1109/61.796227

R.Hooshmand, M. Banejad, Application of Fuzzy Logic in Fault Diagnosis in Transformers using Dissolved Gas based on Different Standards,World Academy of Science, Engineering and Technology, 17, 2006.
http://dx.doi.org/10.5370/jeet.2008.3.3.293


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize