A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis

Abstract Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers.

[1]  V. Miranda,et al.  An interpretation of neural networks as inference engines with application to transformer failure diagnosis , 2004, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[2]  Hari Om Gupta,et al.  Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis , 2012 .

[3]  V. G. Arakelian,et al.  Effective diagnostics for oil-filled equipment , 2002 .

[4]  Michel Duval,et al.  New techniques for dissolved gas-in-oil analysis , 2003 .

[5]  A. Abu-Siada,et al.  A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[6]  S. M. Islam,et al.  Significance of cellulose power transformer condition assessment , 2011, IEEE Transactions on Dielectrics and Electrical Insulation.

[7]  D.V.S.S. Siva Sarma,et al.  ANN approach for condition monitoring of power transformers using DGA , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[8]  A. Abu-Siada,et al.  Standardization of DGA interpretation techniques using fuzzy logic approach , 2012, 2012 IEEE International Conference on Condition Monitoring and Diagnosis.

[9]  Vladimiro Miranda,et al.  Transformer failure diagnosis by means of fuzzy rules extracted from Kohonen Self-Organizing Map , 2012 .

[10]  P. Verma,et al.  A review of intelligent diagnostic methods for condition assessment of insulation system in power transformers , 2008, 2008 International Conference on Condition Monitoring and Diagnosis.

[11]  Hari Om Gupta,et al.  Integrating AI based DGA fault diagnosis using Dempster–Shafer Theory , 2013 .

[12]  Ieee Standards Board.  IEEE guide for the interpretation of gases generated in oil-immersed transformers , 1992 .

[13]  Gerard Ledwich,et al.  A novel fuzzy logic approach to transformer fault diagnosis , 2000 .

[14]  A. Abu-Siada,et al.  A Novel Online Technique to Detect Power Transformer Winding Faults , 2012, IEEE Transactions on Power Delivery.

[15]  T.R. Blackburn,et al.  Comparative study and analysis of DGA methods for mineral oil using fuzzy logic , 2007, 2007 International Power Engineering Conference (IPEC 2007).

[16]  Liu Xianyong,et al.  Research on on-line DGA using FTIR [power transformer insulation testing] , 2002, Proceedings. International Conference on Power System Technology.

[17]  Fu Yang,et al.  Comprehensive method detecting the status of the transformer based on the artificial intelligence , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[18]  Pavlos S. Georgilakis,et al.  Condition monitoring and assessment of power transformers using computational intelligence. W.H. Tang, Q.H. Wu. Springer, London (2011). , 2011 .

[19]  R. Rogers.  IEEE and IEC Codes to Interpret Incipient Faults in Transformers, Using Gas in Oil Analysis , 1978, IEEE Transactions on Electrical Insulation.

[20]  Zheng Li,et al.  Diagnosis of power transformer faults on fuzzy three-ratio method , 2005, 2005 International Power Engineering Conference.

[21]  Q. H. Wu,et al.  Dissolved gas analysis using evidential reasoning , 2005 .

[22]  A. Abu-Siada,et al.  Fuzzy logic approach to identify transformer criticality using dissolved gas analysis , 2010, IEEE PES General Meeting.

[23]  P. J. Griffin,et al.  Neural net and expert system diagnose transformer faults , 2000 .

[24]  A. Abu-Siada,et al.  A Novel Fuzzy-Logic Approach for Furan Estimation in Transformer Oil , 2012, IEEE Transactions on Power Delivery.