Aging evaluation and moisture prediction of oil-immersed cellulose insulation in field transformer using frequency domain spectroscopy and aging kinetics model

Liu, Jiefeng; Fan, Xianhao; Zhang, Yiyi; Zhang, Chaohai; Wang, Zixiao

doi:10.1007/s10570-020-03242-2

Cited by 30 publications

(22 citation statements)

References 36 publications

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“…The oil-immersed power transformer is an essential equipment of the power system for power conversion, distribution, and transmission [1]. It is generally accepted that the remaining life of the transformer is mainly determined by the aging condition of the cellulose insulation [2]- [8].…”

Section: Introductionmentioning

confidence: 99%

Investigation on Formation Mechanisms of Methanol During Cellulose Insulation Aging Based on Molecular Dynamics Simulation

et al. 2021

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In recent years, methanol has been proposed as a chemical indicator to assess the aging condition of cellulose insulation in oil-immersed power transformers. However, the formation mechanisms of methanol during cellulose degradation are not clear enough. In this paper, such formation mechanisms were studied using molecular dynamics simulation. Three main formation pathways of methanol were found and discussed. Analysis of the Mayer bonds of cellulose revealed that each atom of cellulose contributes differently to methanol formation. The methylol groups on the 5-carbon atom of the 1-pyran ring and 4-pyran ring of cellulose are more readily detached to form methanol molecules. In addition, changes in the amount of methanol molecule with simulation time were investigated. The results indicated that the separate existence of initial moisture and oxygen has no significant effect on the formation of methanol, while the coexistence of initial moisture and initial oxygen catalyzes the conversion of cellulose degradation products to methanol. The findings reported in this paper can provide a valuable theoretical basis for further studies on methanol as an indicator to evaluate the residual life of cellulose insulation. INDEX TERMS Cellulose, methanol, molecular dynamics, cellulose insulation, formation mechanisms.

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Section: Introductionmentioning

confidence: 99%

Investigation on Formation Mechanisms of Methanol During Cellulose Insulation Aging Based on Molecular Dynamics Simulation

et al. 2021

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“…Then, establishing the quantitative relationship between these feature parameters and insulating states, and, finally, the insulating status of the oil-immersed insulation can be evaluated by the quantity relationship. Existing studies [11,12] indicated that the insulation conditions of power transformers could be affected by various factors, such as moisture, thermal, oxygen, and acids.…”

Section: Introductionmentioning

confidence: 99%

“…As for the establishment of the quantitative relationship between feature parameters and the insulating states, the traditional methods (such as fitting analysis [12]) achieve it by using the small number of samples, which were faced with the limitation of simple classification (diagnosis) basis, no generalization ability, and difficulty in extrapolation. Fortunately, the support vector machine (SVM) combined with the genetic algorithm (GA) can be utilized to overcome these issues.…”

Section: Introductionmentioning

confidence: 99%

Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm

Zhang

Fan

et al. 2020

Polymers

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The support vector machine (SVM) combined with the genetic algorithm (GA) has been utilized for the fault diagnosis of transformers since its high accuracy. In addition to the fault diagnosis, the condition assessment of transformer oil-immersed insulation conveys the crucial engineering significance as well. However, the approaches for getting GA-SVM used to the moisture prediction of oil-immersed insulation have been rarely reported. In view of this issue, this paper pioneers the application of GA-SVM and frequency domain spectroscopy (FDS) to realize the moisture prediction of transformer oil-immersed insulation. In the present work, a method of constructing a GA-SVM multi-classifier for moisture diagnosis based on the fitting analysis model is firstly reported. Then, the feasibility and reliability of the reported method are proved by employing the laboratory and field test experiments. The experimental results indicate that the reported prediction model might be serviced as a potential tool for the moisture prediction of transformer oil-immersed polymer insulation.

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“…The insulation performance of the oil-immersed power transformer affects the operation level of the power system. While the insulation performance is mainly determined by oil-paper insulation [1]. The deterioration of insulation oil can be solved by replacing oil and filtering oil, but no effective measures have been put forward to solve the deterioration of insulation paper (cellulose insulation).…”

Section: Introductionmentioning

confidence: 99%

A Novel Curve Database for Moisture Evaluation of Transformer Oil-Immersed Cellulose Insulation Using FDS and Exponential Decay Model

et al. 2020

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In recent decades, mo isture evaluation of transformer oil-immersed cellulose insulation has been widely studied by using the frequency domain spectroscopy (FDS) technique. Ho wever, due to the difficult ies in sample preparation, most of the existing evaluation methods are based on a small number of samples, which greatly limits the evaluation accuracy of these methods. To accurately evaluate moisture inside transformer oil-immersed cellulose insulation, a novel method co mbin ing the FDS technique and the exponential decay model is proposed. In current work, the exponential decay model is introduced to analyze the FDS data, which can be used to assess the quantitative relationship between moisture and extracte d parameters. Then, the FDS curves with different moisture can be predicted by using these parameters to form a curve database. Findings reveal the relat ive error of evaluation results in lab condition is less than 10.57% when using the proposed curve database. In that respect, the feasibility of the proposed database is proved for moisture evaluation of transformer oil-immersed cellulose insulation.

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Aging evaluation and moisture prediction of oil-immersed cellulose insulation in field transformer using frequency domain spectroscopy and aging kinetics model

Cited by 30 publications

References 36 publications

Investigation on Formation Mechanisms of Methanol During Cellulose Insulation Aging Based on Molecular Dynamics Simulation

Investigation on Formation Mechanisms of Methanol During Cellulose Insulation Aging Based on Molecular Dynamics Simulation

Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm

A Novel Curve Database for Moisture Evaluation of Transformer Oil-Immersed Cellulose Insulation Using FDS and Exponential Decay Model

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