A comprehensive comparative study of DGA based transformer fault diagnosis using fuzzy logic and ANFIS models

In this work, Dissolved gas Analysis (DGA) has been implemented using soft computing models namely fuzzy logic and Adaptive Neuro fuzzy inference system (ANFIS). DGA has developed as an effective tool for the identification of transformer incipient faults. A number of standards and procedures have evolved over the years making DGA more reliable and user friendly. A comparative study of the two models has been developed based on their ability to circumvent the limitations of the IEC 599 standard, Rogers ratio method and Doernenburg's method. The models have been tested using a reported fault database for their diagnostic capability. Results presented in this paper clearly indicate the superiority of the ANFIS model over the fuzzy system. The ANFIS model presents a reliable system with relative high degree accuracy. ANFIS model being very simple to develop can obviate the limitations of conventional methods of transformer fault diagnosis using DGA.

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

[2]  L. L. Lai,et al.  A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer , 2000 .

[3]  M. Duval,et al.  Dissolved gas analysis: It can save your transformer , 1989, IEEE Electrical Insulation Magazine.

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

[5]  Ruijin Liao,et al.  An Integrated Decision-Making Model for Condition Assessment of Power Transformers Using Fuzzy Approach and Evidential Reasoning , 2011, IEEE Transactions on Power Delivery.

[6]  S Singh,et al.  Dissolved gas analysis technique for incipient fault diagnosis in power transformers: A bibliographic survey , 2010, IEEE Electrical Insulation Magazine.

[7]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[8]  E Gockenbach,et al.  Intelligent agent-based system using dissolved gas analysis to detect incipient faults in power transformers , 2010, IEEE Electrical Insulation Magazine.

[9]  Yilu Liu,et al.  Wavelet Networks in Power Transformers Diagnosis Using Dissolved Gas Analysis , 2009, IEEE Transactions on Power Delivery.

[10]  W.H. Tang,et al.  An evidential reasoning approach to transformer condition assessments , 2004, IEEE Transactions on Power Delivery.

[11]  Michel Duval,et al.  Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases , 2001 .

[12]  Moein Parastegari,et al.  Adaptive neuro-fuzzy inference system approach for simultaneous diagnosis of the type and location of faults in power transformers , 2012, IEEE Electrical Insulation Magazine.

[13]  Xiaohui Li,et al.  DGA Interpretation Scheme Derived From Case Study , 2011, IEEE Transactions on Power Delivery.

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

[15]  R. Naresh,et al.  An Integrated Neural Fuzzy Approach for Fault Diagnosis of Transformers , 2008, IEEE Transactions on Power Delivery.

[16]  Mohammad Golkhah,et al.  Artificial neural networks applied to DGA for fault diagnosis in oil-filled power transformers , 2011 .

[17]  N. K. DHOTE,et al.  Diagnosis of Power Transformer Faults based on Five Fuzzy Ratio Method , 2012 .

[18]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..