Interpreting First Anti-resonance of FRA Responses Through Low Frequency Transformer Modelling
FRA is an effective detection method for mechanical deformation of transformer windings; however the challenge of applying the FRA technique lies in the correct interpretation of FRA measurement results. Modelling and simulation can help understand the FRA characteristics and in this respect much research work are needed especially to develop the modelling capability for three-phase three-winding auto-transformers under the condition of single phase excitation such as the FRA measurement set-up. This paper is a sister paper to  which extends the discussion on how to interpret low frequency FRA features especially the first anti-resonance. Through our previous publication, it has become a common knowledge nowadays that the 1st anti-resonance of FRA is produced by the coupling between the core inductance and the equivalent winding capacitance, though a quantitative analysis is carried out in this paper to prescribe an analytical equation first time showing the linear composition of the equivalent winding capacitance made from winding series capacitances and inter-winding capacitances. It is necessary to understand that although shifts of resonance (and/or anti-resonance) frequencies are regarded as the key indicators of winding deformation, the frequency shift, Δf, of the resonance (and/or anti-resonance) before (f1) and after (f2) the deformation, is actually not the best indicative parameter as Δf cannot be related to the change of electrical parameters in the model, instead the ratio, f2/f1 has a well-defined quantitative linkage with the winding inductances and capacitances. It is therefore recommended to use the frequency ratio, f2/f1, of the resonance (and/or anti-resonance) before (f1) and after (f2) the deformation for FRA interpretation.
KeywordsFRA Interpretation Transformer Modelling
- 7.Cheng, B., Crossley, P., Wang, Z.D., Jarman, P., Roxborough, A.: Interpretation of FRA results through low frequency transformer modelling. In: IEEE 2nd International Conference on Electrical Materials and Power Equipment, Guangzhou, China (April 2019)Google Scholar