Enhancement of Power Transformer State of Health Diagnostics Based on Fuzzy Logic System of DGA

Dissolved Gas Analysis (DGA) of liquid insulation is an effective means for diagnosing power transformers. The gas contents in insulating oil can be gathered on-line and off-line to indicate the health condition of the transformers, thereafter there are many interpretations of the gas contents. In this work, Seven-fuzzy interpretation modules are individually established, tested and lately combined to monitor power transformers' health. The developed method incorporates trending of the concentration of the dissolved gases over the operating life. The approach processes current and/or historical DGA data, using the 7-developed logic modules, to determine the current state of a transformer, provide information regarding the fault type, fault probability, fault severity and recommended future sampling interval in addition to operating procedure, consistent with industry standards. The developed diagnosis system has been validated using 1290 samples from fresh and previously tested mineral oil filled transformers. The proposed system is proved, based on field data, to be 99% accurate in identifying transformers being in normal or abnormal operation. For the cases where a transformer fault was known, the proposed technique has less than 2% inaccuracy in recognizing the fault's type in comparison to other approaches discussed in literature.

[1]  Qian Zheng,et al.  DGA based insulation diagnosis of power transformer via ANN , 2000, Proceedings of the 6th International Conference on Properties and Applications of Dielectric Materials (Cat. No.00CH36347).

[2]  Hasmat Malik,et al.  Fuzzy-Logic Applications in Transformer Diagnosis Using Individual and Total Dissolved Key Gas Concentrations , 2012 .

[3]  Saher Rady Farag.  Monitoring Power Transformer Using Fuzzy Logic , 2013 .

[4]  Kamel A. Shoush,et al.  Diagnostic Tool for Transformer Fault Detection Based on Dissolved Gas Analysis , 2014 .

[5]  Sherif S. M. Ghoneim,et al.  Artificial Neural Networks for Power Transformers Fault Diagnosis Based on IEC Code Using Dissolved Gas Analysis , 2015 .

[6]  J.G. Rolim,et al.  A fuzzy system for detection of incipient faults in transformers based on the dissolved gas analysis of insulating oil , 2005, 2005 5th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

[7]  A. Abu-Siada,et al.  A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[8]  M. Duval,et al.  The duval triangle for load tap changers, non-mineral oils and low temperature faults in transformers , 2008, IEEE Electrical Insulation Magazine.

[9]  Mahdi Banejad,et al.  Application of Fuzzy Logic in Fault Diagnosis in Transformers using Dissolved Gas based on Different Standards , 2008 .

[10]  Vasant Honavar,et al.  Condition Data Aggregation with Application to Failure Rate Calculation of Power Transformers , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[11]  Hui Ma,et al.  Evaluating the accuracy of different DGA techniques for improving the transformer oil quality interpretation , 2011, AUPEC 2011.

[12]  Decun Dong,et al.  Global fault diagnosis method of traction transformer based on Improved Fuzzy Cellular Neural Network , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[13]  Hossam A. Nabwey,et al.  Fault Diagnosis of Power Transformer Based on Fuzzy Logic, Rough Set theory and Inclusion Degree Theory , 2010 .

[14]  André Nunes De Souza,et al.  The application of neural networks to the analysis of dissolved gases in insulating oil used in transformers , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

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

[16]  Haripriya H. Kulkarni,et al.  Modeling of Transformer DGA using IEC & Fuzzy Based Three Gas Ratio Method , 2014 .

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

[18]  F. Faghihi,et al.  A New Approach for Transformer Incipient Fault Diagnosis Based on Dissolved Gas Analysis (DGA) , 2016 .