A Non-Destructive Optical Method for the DP Measurement of Paper Insulation Based on the Free Fibers in Transformer Oil

Lei, Peng; Fu, Qiang; Zhao, Yaohong; Qian, Yihua; Chen, Tiansheng; Fan, Shengping

doi:10.3390/en11040716

Cited by 9 publications

(8 citation statements)

References 17 publications

(20 reference statements)

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“…Increasing requirements for appropriate tools allowing diagnosing of power transformers non-destructively and reliably has promoted the development of modern complementary diagnostic techniques [1,2]. Research activities are still in progress worldwide to improve these techniques [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Impact of Low Molecular Weight Acids on Oil Impregnated Paper Insulation Degradation

et al. 2018

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Aging of a power transformer's insulation system produces carboxylic acids. These acids-acetic, formic and levulinic-are absorbed by the paper insulating material, thus accelerating the degradation of the whole insulation system. In this contribution, the effect of these acids on the aging of oil-impregnated paper insulation used in power transformer is reported. A laboratory aging experiment considering different concentrations of these three acids was performed to assess their effect on the insulation system's degradation. Each acid was individually mixed with virgin oil, and a mixture of acids was also blended with oil. The paper's degradation was assessed by the degree of polymerization (DPv). It was found that the DPv of paper aged with formic acid decreased much faster in comparison to the other acids.

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Section: Introductionmentioning

confidence: 99%

Impact of Low Molecular Weight Acids on Oil Impregnated Paper Insulation Degradation

et al. 2018

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“…An additional operation, called update, is performed to preserve the average value of the original signal, according to (3).…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…For these purposes, maintenance policies and procedures are planned and applied to ensure the least interruption of such equipment [1][2][3]. In fact, any failure in this equipment can affect the whole network, compromise other elements in the grid and generate significant economic impacts [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Due to its complexity and importance, the problems of aging degree of paper insulation has been object of study in many recent works [2][3][4]. Several other tests of insulation items have been an important part of transformers fault diagnosis systems, with emphasis on chromatographic oil-dissolved gas analysis, namely dissolved gas analysis (DGA) [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

et al. 2018

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Transformers are one of the most important part in a power system and, especially in key-facilities, they should be closely and continuously monitored. In this context, methods based on the dissolved gas ratios allow to associate values of gas concentrations with the occurrence of some faults, such as partial discharges and thermal faults. So, an accurate prediction of oil-dissolved gas concentrations is a valuable tool to monitor the transformer condition and to develop a fault diagnosis system. This study proposes a nonlinear autoregressive neural network model coupled with the discrete wavelet transform for predicting transformer oil-dissolved gas concentrations. The data fitting and accurate prediction ability of the proposed model is evaluated in a real world example, showing better results in relation to current prediction models and common time series techniques.

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“…It is widely recognized that failures in power transformers usually lead to long outage times and great repair costs. Thus, substantial efforts have been devoted to diagnosing anomalous operation conditions in power transformers after they have been put into service [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

Zhou

et al. 2018

Energies

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DC bias is a great threat to the safe operation of power transformers. This paper deals with a new vibration-based technique to diagnose DC bias in power transformers. With this technique, the DC bias status of power transformers can be automatically recognized. The vibration variation process of a 500 kV autotransformer is tested under the influence of DC bias in the monopole trail operation stage of a ±800 kV HVDC transmission system. Comparison of transformer vibration under normal and DC-biased conditions is conducted. Three features are proposed and are validated by sensitivity analysis. The principal component analysis method is employed for feature de-correlation and dimensionality reduction. The least square support vector machine algorithm is used and verified successful in DC bias recognition. A remote on-line monitoring device based on the proposed algorithm is designed and applied in field DC bias diagnosis of power transformers. The suggested diagnostic algorithm and monitoring device could be useful in targeted DC bias control and improving the safe operation level of power transformers.

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A Non-Destructive Optical Method for the DP Measurement of Paper Insulation Based on the Free Fibers in Transformer Oil

Cited by 9 publications

References 17 publications

Impact of Low Molecular Weight Acids on Oil Impregnated Paper Insulation Degradation

Impact of Low Molecular Weight Acids on Oil Impregnated Paper Insulation Degradation

Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

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