On-Line Monitoring of Transformer Vibration and Noise Based on DC Magnetic Bias

Liu, Bowei; Hai, Ma; Zhou, Lixing

doi:10.1109/isdea.2013.498

Cited by 6 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A large amount of research has shown that there is a high correlation between neutral DC, vibration and noise under the condition of DC bias [26,27]. For evaluating the Pearson correlation coefficient, the criteria are shown in Table 1 [28].…”

Section: Synchronous Monitoring Of Data Correlationmentioning

confidence: 99%

Identification of Abnormal Data for Synchronous Monitoring of Transformer DC Bias Based on Multiple Criteria

Kou,

Lin,

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

Seriously abnormal data exist in the synchronous monitoring data of transformer DC bias, which causes serious data feature contamination and even affects the identification of transformer DC bias. For this reason, this paper aims to ensure the reliability and validity of synchronous monitoring data. This paper proposes an identification of abnormal data for the synchronous monitoring of transformer DC bias based on multiple criteria. By analyzing the abnormal data of different types, the characteristics of abnormal data are obtained. Based on this, the abnormal data identification indexes are introduced, including gradient, sliding kurtosis and Pearson correlation coefficient. Firstly, the Pauta criterion is used to determine the threshold of the gradient index. Then, gradient is used to identify the suspected abnormal data. Finally, the sliding kurtosis and Pearson correlation coefficient are used to identify the abnormal data. Data for synchronous monitoring of transformer DC bias in a certain power grid are used to verify the proposed method. The results show that the accuracy of the proposed method in identifying mutated abnormal data and zero-value abnormal data is claimed to be 100%. Compared with traditional abnormal data identification methods, the accuracy of the proposed method is significantly improved.

show abstract

Section: Synchronous Monitoring Of Data Correlationmentioning

confidence: 99%

Identification of Abnormal Data for Synchronous Monitoring of Transformer DC Bias Based on Multiple Criteria

Kou,

Lin,

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Aiming to deal with nonstationary signals like vibration signals, many methods have been proposed, such as, empirical mode decomposition (EMD) and the Hilbert spectrum [9], [10], Fourier decomposition method (FDM) [11], a new formulation of the FDM using the discrete cosine transform [12]. The vibration characteristics before and after DC bias are compared and analyzed in [13]- [15], and the characteristic quantities of DC bias are obtained in the time domain, frequency domain, time-frequency domain. However, the obtained characteristic quantities are not further applied to DC bias state identification of transformers.…”

Section: Introductionmentioning

confidence: 99%

State Identification of Transformer Under DC Bias Based on Wavelet Singular Entropy

et al. 2021

View full text Add to dashboard Cite

To identify the DC bias state of transformers, a DC bias state identification method of transformer based on wavelet singular entropy is proposed in this paper. The vibration principle of transformers under DC bias has been analyzed. By combining continuous wavelet transform, singular value decomposition, and information entropy, the analysis method of wavelet singular entropy is proposed. The vibration signal of the transformer before and after DC bias is transformed by continuous wavelet transform, and then the wavelet time-frequency diagram is compared and analyzed. Furthermore, the DC bias state of the transformer is identified by wavelet singular entropy of vibration signal. The wavelet singular entropy under different states is in different numerical ranges, and the wavelet singular entropy of vibration signal under DC bias is significantly greater than that under no DC bias. The wavelet singular entropy effectively reflects the DC bias state of transformers. Then the proposed method is applied to a 500 kV transformer and a 220 kV transformer in the China Southern Power Grid. The results show that the proposed method can accurately and effectively identify the DC bias state of transformers.

show abstract

“…Tradition methods like Epstein frame is only performed in the off-line condition. Lately, several on-line techniques for measuring the DC flux have been proposed [4,5]. Whereas, the forth-mentioned methods mainly focused on the supervened magnetisation behaviours, those are the indirect online method as measurement object is not the magnetic flux but quantities like the acoustic vibration of the transformer.…”

mentioning

confidence: 99%

Online detection approach for direct current flux of power transformer

Cao¹,

Cheng

Khan

2017

Electron. lett.

View full text Add to dashboard Cite

Direct current (DC) flux in power transformer initiates its abnormal operation, and can eventually lead to the transformer damage. A new fluxgate sensor structure to detect the transformer's DC flux is presented. A magnetic shunt path is designed to extract fractional flux from the transformer's core limb. The DC flux can be obtained by measuring the demodulation outcomes of the output signal from the sensor's detecting coil, for there is a monotonic relation between them. The DC flux can be measured by the demodulation of output from the sensor's detecting coil. As verification, the experiment has been implemented with a 10 kVA power transformer. Measurement results show that when the external DC current, used to produce the DC flux in the transformer, varies from 0.2 to 2.5 A, the magnetic modulated output signal changes significantly and the RMS value is monotonously changing with the excited DC current.

show abstract

On-Line Monitoring of Transformer Vibration and Noise Based on DC Magnetic Bias

Cited by 6 publications

References 0 publications

Identification of Abnormal Data for Synchronous Monitoring of Transformer DC Bias Based on Multiple Criteria

Identification of Abnormal Data for Synchronous Monitoring of Transformer DC Bias Based on Multiple Criteria

State Identification of Transformer Under DC Bias Based on Wavelet Singular Entropy

Online detection approach for direct current flux of power transformer

Contact Info

Product

Resources

About