Correlation of hydrogen generation with sparking discharges in a mineral insulating oil

Xiang, Jing; Zhou, Xin; Liu, Q.; Wang, Zhongdong; Hinshaw, John V.; Mavrommatis, P.

doi:10.1109/mei.2018.8345356

Cited by 12 publications

(2 citation statements)

References 10 publications

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“…However, in the paper-oil system, hydrogen production increases and reaches about 85% [32]. Hydrogen is also produced as a result of sparking fault conditions, and its concentration is directly and linearly related to the number of sparks [33].…”

Section: Hydrogen (H 2 )mentioning

confidence: 99%

Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators

et al. 2021

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Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of the cellulose. Since the determination of the DP index is complex and time-consuming and requires the transformer to be taken out of service, utilities prefer indirect and non-invasive methods of determining the DP based on the byproduct of cellulose aging. This paper analyzes solid insulation degradation by measuring the furan concentration, recently introduced methanol, and dissolved gases like carbon oxides and hydrogen, in the insulating oil. A group of service-aged distribution transformers were selected for practical investigation based on oil samples and different kinds of tests. Based on the maintenance and planning strategy of the power utility and a weighted combination of measured chemical indicators, a neural network was also developed to categorize the state of the transformer in certain classes. The method proved to be able to improve the diagnostic capability of chemical indicators, thus providing power utilities with more reliable maintenance tools and avoiding catastrophic failure of transformers.

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Section: Hydrogen (H 2 )mentioning

confidence: 99%

Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators

et al. 2021

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“…The contents of hydrogen (H 2 ), methane (CH 4 ), ethane (C 2 H 6 ), ethylene (C 2 H 4 ), acetylene (C 2 H 2 ) and other gases are closely related to the operation status of the transformer. The operation status of the transformer is analyzed according to the dissolved gas in the oil, which is called dissolved gas analysis(DGA) [13][14][15][16][17][18] .In machine learning algorithm, random forest algorithm is a good big data algorithm, which needs few parameters, fast running speed, high prediction accuracy and good robustness, and does not need to filter and reduce the original data [19] .In this paper, dissolved gases H 2 , CH 4 , C 2 H 6 , C 2 H 4 , C 2 H 2 and their possible ratios…”

Section: Introductionmentioning

confidence: 99%

Study on Chromatographic Condition Assessment of Transformer Oil Based on Random Forest Model

Zhou¹,

Ma²,

Luo³

et al. 2021

dteees

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In order to solve the problem that the accuracy of transformer condition assessment based on oil chromatographic data is not high, this paper adopts random forest to evaluate transformer state. In order to prevent the attribute values carried by the decision tree of the basic unit in the evaluation model from being too few and thus affecting the evaluation accuracy. The ratio of H 2 , CH 4 , C 2 H 6 , C 2 H 4 , C 2 H 2 , total hydrocarbon and various characteristic gases is used as the characteristic quantity, a stochastic forest optimization model for oil chromatography state assessment was constructedimmediately following the heading. It is compared with BP neural network and SVM, the test results of field oil chromatogram data show that the accuracy of random forest state assessment is 88.5%, which is better than BP and SVM, and the random forest assessment model has faster assessment speed, This paper provides technical support for the fast and accurate condition assessment of transformer.

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Study on Change Law of Pressure and Hydrogen Gas Under Partial Discharge Defects in Transformer Bushing

Huang,

Luo,

et al. 2023

Lecture Notes in Electrical Engineering

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Correlation of hydrogen generation with sparking discharges in a mineral insulating oil

Cited by 12 publications

References 10 publications

Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators

Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators

Study on Chromatographic Condition Assessment of Transformer Oil Based on Random Forest Model

Study on Change Law of Pressure and Hydrogen Gas Under Partial Discharge Defects in Transformer Bushing

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