New high frequency multi-conductor transmission line detailed model of transformer winding for PD study

The first step of partial discharge (PD) detection is to obtain a model through which the winding behavior is monitored precisely in high frequencies. The multi-conductor transmission line (MTL) model is one of the best models conducting the PD studies. It is worth to say that one of the main problems of the MTL model is that it considers the windings in a parallel way as well as equal length. Solving the mentioned problem can decrease the simulation error relating to the PD detection. This paper presents an accurate approach in order to solve the problem. This modified MTL model uses the circular effect of windings. Considering the windings circular is one of the important parameters which should be evaluated with respect to PD signal propagation. This method has been applied on a 20 kV transformer winding. The simulation and experimental results show the powerful performance of circular multi-conductor transmission line (CMTL) model in comparison with the MTL model.

[1]  R. Candela,et al.  PD recognition by means of statistical and fractal parameters and a neural network , 2000 .

[2]  C. Koley,et al.  A methodology for identification and localization of partial discharge sources using optical sensors , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[3]  K X Lai,et al.  Application of data mining on partial discharge part I: predictive modelling classification , 2010, IEEE Transactions on Dielectrics and Electrical Insulation.

[4]  V. Venegas,et al.  Calculation of electrical parameters for transient overvoltage studies on electrical machines , 2003, IEEE International Electric Machines and Drives Conference, 2003. IEMDC'03..

[5]  G.B. Gharehpetian,et al.  Comparison of Transformer Detailed Models for Fast and Very Fast Transient Studies , 2008, IEEE Transactions on Power Delivery.

[6]  Weng Hoe Leong,et al.  Comparison of Known PD Signals With the Developed and Commercial HFCT Sensors , 2007, IEEE Transactions on Power Delivery.

[7]  G. Montanari,et al.  Digital detection and fuzzy classification of partial discharge signals , 2002 .

[8]  Tao Wang,et al.  Calculation of Electrical Parameters for Studies on Propagation Characteristic of PD along Transformer Winding , 2008, 2008 International Conference on Computer and Electrical Engineering.

[9]  M. S. Naderi,et al.  Three-dimensional simulation of PD source allocation through TDOA method , 2012, The 4th Conference on Thermal Power Plants.

[10]  Seyed Mohammad Hassan Hosseini,et al.  Transformer Winding Modeling based on Multi-Conductor Transmission Line Model for Partial Discharge Study , 2014 .

[11]  S. Okabe,et al.  Partial discharge signal propagation characteristics inside the winding of oil-immersed power transformer using the equivalent circuit of winding model in the oil , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[12]  Yan Li,et al.  Calculation of capacitance and inductance parameters based on FEM in high-voltage transformer winding , 2011, 2011 International Conference on Electrical Machines and Systems.

[13]  Brian G. Stewart,et al.  Study of propagation behaviour of Partial Discharge acoustic signals in a 3-D model tank , 2009, 2009 44th International Universities Power Engineering Conference (UPEC).

[14]  M D Judd,et al.  Simultaneous measurement of partial discharges using IEC60270 and radio-frequency techniques , 2011, IEEE Transactions on Dielectrics and Electrical Insulation.

[15]  Hui Ma,et al.  Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.