Determination of nanosecond pulse parameters on transfer function measurement for power transformer winding deformation
- Authors: Zhao, Zhongyong , Yao, Chenguo , Hashemnia, Naser , Islam, Syed
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 23, no. 6 (2016), p. 3761-3770
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- Description: Transfer function method is now a widely acceptable tool to diagnose transformer winding deformations. A sweep frequency sine wave generator is often used to excite the different modes of resonance and anti-resonances. However, it is time consuming. Nanosecond square wave pulse signal offers an alternative that can serve the same objective. However, as so far, there is no certain criterion for selecting pulse parameters. This paper provides a comprehensive method for the determination of nanosecond square wave pulse parameters for transfer function evaluation of power transformer for winding deformation studies.
Diagnosing transformer winding deformation faults based on the analysis of binary image obtained from FRA signature
- Authors: Zhao, Zhongyong , Yao, Chenguo , Tang, Chao , Li, Chengxiang , Yan, Fayou , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 40463-40474
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- Description: Frequency response analysis (FRA) has been widely accepted as a diagnostic tool for power transformer winding deformation faults. Typically, both amplitude-frequency and phase-frequency signatures are obtained by an FRA analyzer. However, most existing FRA analyzers use only the information on amplitude-frequency signature, while phase-frequency information is neglected. It is also found that in some cases, the diagnostic results obtained by FRA amplitude-frequency signatures do not comply with some hard evidence. This paper introduces a winding deformation diagnostic method based on the analysis of binary images obtained from FRA signatures to improve FRA outcomes. The digital image processing technique is used to process the binary image and obtain a diagnostic indicator, to arrive at an outcome for interpreting winding faults with improved accuracy.
Improved method to obtain the online impulse frequency response signature of a power transformer by multi scale complex CWT
- Authors: Zhao, Zhongyong , Tang, Chao , Yao, Chenguo , Zhou, Qu , Xu, Lingna , Gui, Yingang , Islam, Syed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 48934-48945
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- Description: Online impulse frequency response analysis (IFRA) has proven to be a promising method to detect and diagnose the transformer winding mechanical faults when the transformer is in service. However, the existing fast Fourier transform (FFT) is actually not suitable for processing the transient signals in online IFRA. The field test result also shows that the IFRA signature obtained by FFT is easily distorted by noise. An improved method to obtain the online IFRA signature based on multi-scale complex continuous wavelet transform is proposed. The electrical model simulation and online experiment indicate the superiority of the wavelet transform compared with FFT. This paper provides guidance on the actual application of the online IFRA method.
Interpretation of transformer winding deformation fault by the spectral clustering of FRA signature
- Authors: Zhao, Zhongyong , Tang, Chao , Chen, Yu , Zhou, Qu , Yao, Chenguo , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Electrical Power and Energy Systems Vol. 130, no. (2021), p.
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- Description: Frequency response analysis (FRA) has been accepted as a widely used tool for the power industry. The interpretation of FRA can be achieved by the conventional mathematical indicators-based method, which is mostly used in the past. The newly developing artificial intelligence (AI)-based method also provides an alternative interpretation. However, in most reported AI techniques, the features of FRA signatures are directly input into the AI model to obtain the classification results. Few studies have concentrated on the separability of winding deformation faults. In this context, a spectral clustering algorithm is studied to aid in FRA interpretation. The electrical model simulation and experimental tests are performed. The FRA data processing results verify the feasibility, effectiveness and superiority of the proposed method. © 2021 Elsevier Ltd