Internal Fault Identification and Classification of Transformer with the Aid of Radial Basis Neural Network (RBNN)

This paper deals with the identification and classification of internal fault current of power transformer occurring during the time of abnormal condition. The need of internal fault current classification is to avoid the complexity of the fault category. In this paper, the inrush current and short circuit current of the transformer internal windings are classified from the nominal current. Before the classification process, the analytical model parameters based identification of inrush current is described. The analytical model parameters considered are wave shape and wave peak of the current. The output of the power transformer is applied to classifier and then, the shape and peak of the waveform are extracted from the classifier. Here, an artificial intelligence based radial basis neural network (RBNN) classifier is used to extract the wave parameters. In the RBNN, the Gaussian function is considered as an activation function. The proposed internal fault identification and classification technique is implemented and tested with different ratings of transformer, and the fault classification performances are evaluated. Then, the evaluated results are compared with the feed-forward network.

[1]  Yaonan Wang,et al.  Active Diverse Learning Neural Network Ensemble Approach for Power Transformer Fault Diagnosis , 2010, J. Networks.

[2]  J. Faiz,et al.  Three- and Two-Dimensional Finite-Element Computation of Inrush Current and Short-Circuit Electromagnetic Forces on Windings of a Three-Phase Core-Type Power Transformer , 2008, IEEE Transactions on Magnetics.

[3]  G. Diaz,et al.  Analytical approach to internal fault simulation in power transformers based on fault-related incremental currents , 2006, IEEE Transactions on Power Delivery.

[4]  S. R. Paraskar,et al.  Discrimination between inrush and fault condition in transformer: a probabilistic neural network approach , 2012 .

[5]  Gordon Kettleborough,et al.  Modeling and Calculating the In-Rush Currents in Power Transformers 1 , 2005 .

[6]  Mohammadreza Barzegaran,et al.  Detecting the Position of Winding Short Circuit Faults in Transformer Using High Frequency Analysis , 2008 .

[7]  Mohammad Mirzaie,et al.  Calculation and Analysis of Transformer Inrush Current Based on Parameters of Transformer and Operating Conditions , 2011 .

[8]  Guanrong Chen,et al.  A time-varying complex dynamical network model and its controlled synchronization criteria , 2004, IEEE Trans. Autom. Control..

[9]  Yu Cui,et al.  A sequential phase energization technique for transformer inrush current reduction - Part I: Simulation and experimental results , 2005 .

[10]  A. Srinivasula Reddy,et al.  EVALUATION OF TRANSFORMER FAULTS USING DOUBLE FOURIER SERIES - A FASTEST METHOD FOR FIELD COMPUTATIONS , 2008 .

[11]  Manoj Tripathy,et al.  Improved transformer protection using probabilistic neural network and power differential method , 2010 .

[12]  Asif Islam,et al.  Detection of Mechanical Deformation in Old Aged Power Transformer Using Cross Correlation Co-Efficient Analysis Method , 2011 .

[13]  Mostafa Alinezhad,et al.  Detection of internal fault in differential transformer protection based on fuzzy method , 2011 .

[14]  Mehdi Nafar,et al.  A Sensitive Method for Identifying winding turn to turn faults in Power Transformer , 2011 .

[15]  H. P. Berg,et al.  Reliability of main transformers , 2011 .

[16]  D. Menniti,et al.  Coordinated control of phase shifters in multiarea power system to improve load-frequency dynamic performance , 2012, 2012 16th IEEE Mediterranean Electrotechnical Conference.

[17]  Almoataz Youssef Abdelaziz.  Classification of Transient Phenomena in Power Transformers Based on a Wavelet-ANN Approach , 2011 .

[18]  He Jial.  SIMULATING METHOD OF MAGNETIZING INRUSH CURRENT OF POWER TRANSFORMERS USING CONCEPT OF INSTANTANEOUS POWER , 1999 .

[19]  M. Jamali,et al.  Mitigation of Magnetizing Inrush Current using Sequential Phase Energization Technique , 2011 .

[20]  H. Monsef,et al.  A NEW WAVELET-BASED APPROACH FOR INTERNAL FAULT CURRENT IDENTIFICATION IN POWER TRANSFORMERS , 2008 .

[21]  Junan Lu,et al.  Structure identification of uncertain general complex dynamical networks with time delay , 2009, Autom..

[22]  W.H. Tang,et al.  A Morphological Scheme for Inrush Identification in Transformer Protection , 2009, IEEE Transactions on Power Delivery.

[23]  Adel A. Obed,et al.  A Wavelet Packet Transform-Based Technique for the Discrimination of Inrush Currents from Faults in Three-Phase Transformer , 2011 .

[24]  Wilsun Xu,et al.  A Sequential Phase Energization Method for Transformer Inrush Current Reduction—Transient Performance and Practical Considerations , 2007, IEEE Transactions on Power Delivery.

[25]  SRParaskar,et al.  Discrimination between Inrush and Fault in Transformer: ANN Approach , 2011 .

[26]  Guanrong Chen,et al.  Chaos synchronization of general complex dynamical networks , 2004 .

[27]  Amrita Sinha,et al.  Numerical differential protection of power transformer using ANN as a pattern classifier , 2010, 2010 International Conference on Power, Control and Embedded Systems.

[28]  Narri Yadaiah,et al.  Internal fault detection techniques for power transformers , 2011, Appl. Soft Comput..