MLP neural network-based decision for power transformers fault diagnosis using an improved combination of Rogers and Doernenburg ratios DGA

Abstract Dissolved gas analysis (DGA) is a widely-used method to detect the power transformer faults, because of its high sensitivity to small amount of electrical faults. The DGA is exploited for fault classification tools implementation using the artificial intelligence techniques. In this study, we use the Rogers ratios, the Doernenburg ratios methods and our proposed combination of Rogers and Doernenburg ratios DGA methods as gas signature. The multi-layer perceptron neural network (MLPNN) is applied for decision making. The paper presents a comparative study on one hand for the choice the most appropriate DGA method and to resolve the problem of conflict between the Rogers and Doernenburg ratios methods. On the other hand, it compares the various MLP architectures by comparing two output data types and three hidden layer types with the aim to establish the most appropriate MLP model. Before testing, the proposed structures are trained and tested by the experimental data from Tunisian Company of Electricity and Gas (STEG). The test results suggest that MLPNN ratios combination can generalize better than other MLPNN models. The approach has the advantages of high accuracy. The other advantage is that the model is practically applicable and may be utilized for an automated power transformer diagnosis. The classification accuracies of the MLPNN classifier are compared with fuzzy logic (FL), radial basis function (RBF), K-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification.

[1]  W.H. Tang,et al.  A Probabilistic Classifier for Transformer Dissolved Gas Analysis With a Particle Swarm Optimizer , 2008, IEEE Transactions on Power Delivery.

[2]  M. H. Wang,et al.  A Novel Extension Method for Transformer Fault Diagnosis , 2002, IEEE Power Engineering Review.

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

[4]  J. L. Guardado,et al.  A Comparative Study of Neural Network Efficiency in Power Transformers Diagnosis Using Dissolved Gas Analysis , 2001, IEEE Power Engineering Review.

[5]  Humberto Henao,et al.  Induction machine fault detection using stray flux EMF measurement and neural network-based decision , 2008 .

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

[7]  A. Karami,et al.  Power system transient stability margin estimation using neural networks , 2011 .

[8]  Q. Henry Wu,et al.  Power Transformer Fault Classification Based on Dissolved Gas Analysis by Implementing Bootstrap and Genetic Programming , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Hsing-Chia Kuo,et al.  Symbiotic evolution-based design of fuzzy-neural transformer diagnostic system , 2004 .

[10]  Mohammad Mohammadi,et al.  Radial basis function neural network for power system load-flow , 2008 .

[11]  Hong-Tzer Yang,et al.  Adaptive fuzzy diagnosis system for dissolved gas analysis of power transformers , 1999 .

[12]  V. Miranda,et al.  Improving the IEC table for transformer failure diagnosis with knowledge extraction from neural networks , 2005, IEEE Transactions on Power Delivery.

[13]  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.

[14]  Muhammad Murtadha Othman,et al.  Fuzzy logic application in DGA methods to classify fault type in power transformer , 2010 .

[15]  P. J. Griffin,et al.  An Artificial Neural Network Approach to Transformer Fault Diagnosis , 1996, IEEE Power Engineering Review.

[16]  Yu-Jen Lin.  Explaining critical clearing time with the rules extracted from a multilayer perceptron artificial neural network , 2010 .

[17]  D. N. Vishwakarma,et al.  Application of radial basis function neural network for differential relaying of a power transformer , 2003, Comput. Electr. Eng..

[18]  Taher Niknam,et al.  Using correlation coefficients for locating partial discharge in power transformer , 2011 .

[19]  Chia-Hung Lin,et al.  Grey clustering analysis for incipient fault diagnosis in oil-immersed transformers , 2009, Expert Syst. Appl..

[20]  Khmais Bacha,et al.  Power transformer fault diagnosis based on dissolved gas analysis by support vector machine , 2012 .

[21]  Qian Qing-quan,et al.  Study of a new method for power system transients classification based on wavelet entropy and neural network , 2011 .

[22]  Yu Sun,et al.  Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm , 2008 .

[23]  Shen Zhang,et al.  Improved BP Neural Network for Transformer Fault Diagnosis , 2007 .

[24]  Whei-Min Lin,et al.  Transformer-fault diagnosis by integrating field data and standard codes with training enhancible adaptive probabilistic network , 2005 .

[25]  S. Deng,et al.  Application of multiclass support vector machines for fault diagnosis of field air defense gun , 2011, Expert Syst. Appl..