Transformer impedence circle character based winding deformation online monitoring

This paper introduced a novel on-line transformer winding deformation monitoring technology. That is constructing the locus of the voltage difference on both sides of the transformer changes with the primary side current. Results show that the locus is an ellipse and we name it the transformer impedance circle character. Mathematical methods were applied to analyze the characteristics of the impedance circle, as well as the factors that influence the shape of it. Indicators such as eccentricity ratio and deviation angle, which is free from of running condition but sensitive to internal fault, were established to evaluate the state of winding. Indexes established in normal condition are determined as fingerprint. By comparing the variation in each cycle, the stuff can assess the state of the windings. This technique does not call for any special equipment and has clear judgment standard. Thus, it is possible to realize on-line monitoring. Finally, PSCAD simulation and dynamic simulation experiment verified the practicality and validity of the proposed method.

[1]  Mehdi Bagheri,et al.  Frequency response analysis and short-circuit impedance measurement in detection of winding deformation within power transformers , 2013, IEEE Electrical Insulation Magazine.

[2]  Stephen J. Chapman,et al.  Electric Machinery Fundamentals , 1991 .

[3]  A. Abu-Siada,et al.  A Novel Online Technique to Detect Power Transformer Winding Faults , 2012, IEEE Transactions on Power Delivery.

[4]  Mehdi Bagheri,et al.  Practical challenges in online transformer winding deformation diagnostics , 2011, 2011 2nd International Conference on Electric Power and Energy Conversion Systems (EPECS).

[5]  A. Abu-Siada,et al.  Online Transformer Internal Fault Detection Based on Instantaneous Voltage and Current Measurements Considering Impact of Harmonics , 2017, IEEE Transactions on Power Delivery.

[6]  M.F. El-Naggar,et al.  A novel approach for fault diagnosis of power transformers based on extracting invariant moments , 2008, 2008 Australasian Universities Power Engineering Conference.

[7]  A. Abu-Siada,et al.  A novel algorithm to detect internal transformer faults , 2011, 2011 IEEE Power and Energy Society General Meeting.