Performance evaluation of dissolved gas analysis techniques against measurement errors

Fault diagnosing power transformers based on dissolved gas analysis (DGA) has become an important and a significant tool. There are a number of techniques developed for DGA that are represented extensively in literature. In practical application of DGA, there is a degree of measurement errors in obtained data. These errors are produced from inaccuracy in measurement system, environmental impact, and human errors. In the present study, it is aimed to investigate the sensitivity of different DGA techniques against measurement errors. The considered DGA techniques are IEC Ratio, Duval Triangle, and Pentagon shape. A total number of 380 actual samples were obtained from the Egyptian Electricity Network as well as published reports. Measurement errors are modeled with various levels as a percentage of original data using a random function. The three different DGA techniques are applied for error data and the corresponding diagnostic accuracy is evaluated. The techniques are compared on two stages. The first stage includes the comparison for only main faults, thermal, arcing and partial discharge. The second stage compares the diagnostic accuracy for detailed faults, such as low thermal, medium thermal, high thermal, and so on. Based on the results, the most robust technique is adopted.

[1]  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.

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

[3]  M.A. Izzularab,et al.  On-line diagnosis of incipient faults and cellulose degradation based on artificial intelligence methods , 2004, Proceedings of the 2004 IEEE International Conference on Solid Dielectrics, 2004. ICSD 2004..

[4]  Xiaohui Li,et al.  RMP neural network based dissolved gas analyzer for fault diagnostic of oil-filled electrical equipment , 2011, IEEE Transactions on Dielectrics and Electrical Insulation.

[5]  Laurent Lamarre,et al.  The duval pentagon-a new complementary tool for the interpretation of dissolved gas analysis in transformers , 2014, IEEE Electrical Insulation Magazine.

[6]  Diaa-Eldin A. Mansour,et al.  Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases , 2015, IEEE Transactions on Dielectrics and Electrical Insulation.

[7]  Rafael Paiva Tavares.  Diagnosing faults in power transformers with autoassociative neural networks and mean shift , 2012 .

[8]  Ke Wang,et al.  Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine , 2016, IEEE Transactions on Dielectrics and Electrical Insulation.

[10]  Michel Duval,et al.  A review of faults detectable by gas-in-oil analysis in transformers , 2002 .

[11]  D. A. Mansour.  A new graphical technique for the interpretation of dissolved gas analysis in power transformers , 2012, 2012 Annual Report Conference on Electrical Insulation and Dielectric Phenomena.

[12]  Nagy I. Elkalashy,et al.  Conditional probability-based interpretation of dissolved gas analysis for transformer incipient faults , 2017 .

[13]  Tapan Kumar Saha,et al.  Review of modern diagnostic techniques for assessing insulation condition in aged transformers , 2003 .