Modeling and real-time fault identification in transformers

Abstract In this paper, a different internal fault modeling and an identification algorithm are presented. There has been an increasing concern about turn-to-turn faults in transformers because of the high costs of unexpected outages. It is not always possible to analyze the transformer behavior under such faults at rated conditions, since the tests are highly destructive. To develop transformer internal fault detection technique, a transformer model to simulate internal faults is required. This paper describes a novel technique and methodology for modeling and identifying transformer internal faults by using transmission line method (TLM) and fuzzy reasoning technique based on dynamic principal component analysis (PCA), respectively. The transformer has been modeled considering non-linearities as hysteresis and saturation. Transformer internal fault currents are successfully discriminated from the rated currents. The degree and priority of transformer internal faults are obtained by the proposed method. It is suited for implementation on computers because of no computation complexity. Hence, the proposed algorithm can be used effectively in real-time fault identification problems.

[1]  T. Leibfried,et al.  Monitoring of power transformers using the transfer function method , 1999 .

[2]  K. L. Butler,et al.  Finite Element Analysis of Internal Winding Faults in Distribution Transformers , 2001, IEEE Power Engineering Review.

[3]  M. Condon,et al.  Time-domain modelling of power transformers using modal analysis , 1997 .

[4]  C. Christopoulos,et al.  The Transmission-line Modeling Method: TLM , 1995, IEEE Antennas and Propagation Magazine.

[5]  John F. MacGregor,et al.  Data-based methods for process analysis, monitoring and control , 2003 .

[6]  J. Karhunen.  Optimization criteria and nonlinear PCA neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[7]  C. Christopoulos,et al.  Discrete transform technique for solving nonlinear circuits and equations , 1992 .

[8]  Fredrik Gustafsson,et al.  A comparison of two methods for stochastic fault detection: the parity space approach and principal components analysis , 2003 .

[9]  Eugeniusz Rosolowski,et al.  A self-organizing fuzzy logic based protective relay-an application to power transformer protection , 1997 .

[10]  N.D.R. Sarma,et al.  Characteristics of transformer parameters during internal winding faults based on experimental measurements , 1999, 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333).

[11]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[12]  A. I. Megahed.  A model for simulating internal earth faults in transformers , 2001 .

[13]  Michel Meunier,et al.  A transformer model for winding fault studies , 1994 .

[14]  Eugeniusz Rosolowski,et al.  A multi-criteria fuzzy logic transformer protection , 1997 .

[15]  Javier Fernández,et al.  Electromagnetic model of turn‐to‐turn short circuits in transformers , 2004 .

[16]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[17]  R. K. Aggarwal,et al.  A Novel Approach to the Classification of the Transient Phenomena in Power Transformers Using Combined Wavelet Transform and Neural Network , 2001, IEEE Power Engineering Review.

[18]  M. E. Hamedani Golshan,et al.  A new method for recognizing internal faults from inrush current conditions in digital differential protection of power transformers , 2004 .

[19]  Venkat Venkatasubramanian,et al.  PCA-SDG based process monitoring and fault diagnosis , 1999 .