A New Method for Complex Impedance Measurement of Power Transformers via a Continuous Wavelet Transform

Eduardo Gómez-Luna, John E. Candelo-Becerra and Juan C. Vasquez ()
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Eduardo Gómez-Luna: Grupo de Investigación en Alta Tensión—GRALTA, Escuela de Ingeniería Eléctrica y Electrónica Universidad del Valle, Cali 760015, Colombia
John E. Candelo-Becerra: Departamento de Energía Eléctrica y Automática, Facultad de Minas, Universidad Nacional de Colombia, Sede Medellín, Carrera 80 No. 65-223, Medellín 050041, Colombia
Juan C. Vasquez: Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark

Energies, 2024, vol. 17, issue 23, 1-12

Abstract: The Fourier transform is widely accepted as the time-to-frequency conversion procedure, although it has some limitations. Currently, measurements in the time domain are usually transient (non-periodic waveforms) within a finite window time and discrete (non-continuous) sampled signals. The accuracy of the Fourier transform decreases as the window time and sampling frequency decrease. This is where the wavelet transform proves to be a valuable tool in this analysis. This paper presents a novel method for estimating the complex electrical impedance of power transformers by analyzing transient electrical signals with the continuous wavelet transform. The great importance of knowing the complex electrical impedance of the transformer is that it allows knowing the state and condition of the internal parts, such as the core and the windings, whose behavior depends on the frequency with which the transformer is fed. The wavelet transform is employed in the proposed method to improve the analysis of the frequency response (FRA), following the same procedure commonly used with the Fourier transform. The proposed method is validated by performing an experimental test on a 28 MVA power transformer. The results show that the new method using the continuous wavelet transform is a power tool that enhances the extraction of the total electrical impedance curve (magnitude–phase) compared to the Fourier transform. This enables real-time frequency response analysis in transformers, facilitating accurate diagnosis.

Keywords: wavelet transform; Fourier transform; time domain signals; complex frequency domain signals; non-periodic transients (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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