An Online Data-Driven Technique for the Detection of Transformer Winding Deformations

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Abstract
This paper presents a novel online diagnostics method capable of detecting winding deformations in two-winding single-phase transformers. The main idea is to identify changes in the short-circuit impedance. The combination of 3-D Lissajous curve methods with a Butterworth low-pass filter allows for the accurate determination of winding deformation of large power transformers in real time. The method is very robust and capable of detecting deformations at the early stage even when the measurements are noisy. Only information already available to the differential protection relay is needed. The proposed diagnostics method has been validated with circuit and finite element simulations plus a lab experiment. Results show that the proposed online diagnostics technique has the ability to identify winding deformation problems under severe conditions, such as non-sinusoidal input, nonlinear loading, and measurement noise. Under ideal conditions (no signal noise), the inductive identification error of the proposed online diagnostics method identifies the parameters with less than 0.09% error. When accepting measurement noise of 1%, the error on the identification of inductance is less than 0.13%.

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