Fault detection in a power transformer based on reverberation time

Bjelić, Miloš; Brkovic, Bogdan; Žarković, Mileta; Miljković, Tatjana

doi:10.1016/j.ijepes.2021.107825

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…In case of a mechanical change in the coils or iron core, the system of resistances, inductances, and capacitances of the transformer will differ according to a specific pattern depending on the type of fault, which is reflected in the result of this test and indicates the presence of this type of faults, where the frequency response is indicative of the compounds that make up the transformer system and any difference in these compounds will affect this frequency response [7], [8]. In this test (SFRA), a low-voltage wave with a variable frequency is applied to one end of the coil, and this wave is DIAGNOSTYKA, Vol.…”

Section: Introductionmentioning

confidence: 99%

“…The interpretation of SFRA results is performed by visualization of images obtained using specialized devices [8]. In our case, the distribution transformer of rating 50 kV A, 30V/0 .4KV, three phase, 50 Hz has been specially used in laboratory of Msila university by the authors and his team for carrying out SFRA testing by practically using FRAX101 instrument of megger companyto detect winding displacements in power transformers or faults in the magnetic core.…”

Section: Introductionmentioning

confidence: 99%

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Testing and diagnostic out of the service power transformer using a sweep frequency response analysis (SFRA) comparing with China standard

Salim,

Rouabhi,

Ayad

et al. 2024

Diagnostyka

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A several diagnostic methods used to evaluate and detect any potential issues or faults in the transformer before they cause significant problems. The research proposal focuses on employing the SFRA (Sweep Frequency Response Analysis) technique, renowned for its exceptional sensitivity and diagnostic capabilities. This method serves to identify the mechanical integrity of the transformer's core, winding distortion, and clamping structures by analyzing their electrical transfer functions across a broad frequency spectrum. By utilizing SFRA, the study aims to accurately predict the internal physical condition of the transformer, making it a highly effective and reliable indicator for assessing its overall health. The motivation of this present work is using all experiments of SFRA were conducted and validated on a three-phase 30kV/0.4KV voltage transformer with 50kVA at the laboratory of Msila university. The result of these experiments is presented and discussed in terms of interpretation the analyses based on different standards.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Testing and diagnostic out of the service power transformer using a sweep frequency response analysis (SFRA) comparing with China standard

Salim,

Rouabhi,

Ayad

et al. 2024

Diagnostyka

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“…Bigdeli and Abu-side (2022) used K-means and GOA frequency response methods to diagnose internal mechanical deformation caused by winding short circuits. Bjelić et al (2022) used the reverberation time method to diagnose the transformer winding short circuit, and got a suitable identification. Tarimoradi et al (2022) analyzed the internal mechanical defects of transformer windings from four indexes using the frequency response method.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of transformer winding short circuit based on WKPCA-WM and IPOA-CNN

et al. 2023

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When the winding of the power transformer is short-circuited, the winding will experience constant vibration, which will cause axial instability of the winding, and then lead to winding looseness, deformation, bulge, etc., therefore, a diagnosis method based on the Improved Pelican Optimization Algorithm and Convolutional Neural Network (IPOA-CNN) for short-circuit voiceprint signal of transformer windings is proposed. At the same time, considering the input parameter dimension of deep learning cannot be too high, a new feature parameter selection method is constructed for this model. Firstly, the frequency characteristics of winding acoustic vibration signals are analyzed, and then the characteristic parameters of transformer acoustic signals are extracted by Wavelet Packet Energy Spectrum (WPES) and Mel Frequency Cepstrum Coefficient (MFCC), respectively. Then, the two methods are combined to construct the WM feature extraction algorithm, and the Weighted Kernel Principal Component Analysis (WKPCA) is used to reduce the dimension of the feature to obtain the feature parameters with accurate feature information and low redundancy; Finally, combined with Sobol sequence to optimize the initial population of Pelican Optimization Algorithm (POA), the convolution kernel of Convolutional neural network (CNN) was optimized by IPOA, and the optimal convolution kernel was obtained. The transformer winding short-circuits voiceprint diagnosis models of WKPCA-WM and IPOA-CNN were constructed, which realized the accurate diagnosis of winding short-circuit voiceprint. The validity and feasibility of the method are verified by the acoustic signal data collected in the laboratory.

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“…The converted voltages power the hardware chip, the control chip and the associated output devices, respectively. The conversion module is divided into two main parts: LM7805 converts the 12V DC voltage to SV voltage and uses LM25% chip to convert the DC SV voltage to 3.3V voltage[8].…”

mentioning

confidence: 99%

Development of main transformer winding deformation detection instrument based on sweeping impedance method

Zhao,

Chen,

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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Due to the lack of effective analysis of the short-circuit impedance curve in the main transformer winding deformation detection, the detection results are poor. Therefore, a method for developing a main transformer winding deformation detector based on swept frequency impedance method is proposed. Firstly, by combining power electronics and pulse power technology, a novel nanosecond pulse generator capable of outputting high voltage and stable repetition rate is proposed. Optoelectronic signals are selected as transmission signals to improve response speed and anti-interference ability, and the hardware circuit of the instrument is designed and optimized; Then, the frequency sweep impedance method is used to obtain and analyze the frequency sweep impedance curve and short-circuit impedance value of the transformer, reducing the detection error. The winding deformation detection is completed by comparing the changes before and after the transformer has a short-circuit fault. The results show that the standard deviation of the sweep impedance value of the main transformer winding deformation detector developed using this method is small and the detection accuracy is high.

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Fault detection in a power transformer based on reverberation time

Cited by 8 publications

References 14 publications

Testing and diagnostic out of the service power transformer using a sweep frequency response analysis (SFRA) comparing with China standard

Testing and diagnostic out of the service power transformer using a sweep frequency response analysis (SFRA) comparing with China standard

Fault diagnosis of transformer winding short circuit based on WKPCA-WM and IPOA-CNN

Development of main transformer winding deformation detection instrument based on sweeping impedance method

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