Condition assessment of the health index for power transformer

Haema, Juthathip; Phadungthin, Rattanakorn

doi:10.1109/peam.2012.6612413

Cited by 32 publications

(27 citation statements)

References 1 publication

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“…The transition probabilities obtained by the nonlinear optimization technique show that the predicted HI is quite good and the prediction accuracy could reach up to 95.49%. Based on the predicted HI condition curve, state scale, and recommendations in References [5,6,11,31], the planning for the maintenance, repair, and replacement processes could be considered after 22 years, when the transformer population starts to enter the poor condition. Overall, MM provides a less complex approach for the prediction of transformers HI and can be easily implemented by utilities that utilize HI for their optimal asset management strategy.…”

Section: Discussionmentioning

confidence: 99%

“…HI is defined by Reference [29] as an approach to quantify transformers condition monitoring information for asset management purposes. Nowadays, HI is adopted by most of the utilities in the world [5,7,8,10,11,30]. The conventional concept of HI formulation is based on a scoring method that is based on weighting and ranking techniques [29][30][31].…”

Section: Condition Assessment and Health Index (Hi)mentioning

confidence: 99%

“…Under CBM, a single quantitative assessment known as Health Index (HI) is normally formulated to provide the overall condition of transformers. HI normally consists of multiple input parameters such as oil condition monitoring data, loadings, design, location, and electrical/mechanical integrities [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…condition monitoring data, loadings, design, location, and electrical/mechanical integrities [5][6][7][8][9][10][11]. HI provides a comprehensive condition assessment of transformers as compared to Dissolved Gases Analysis (DGA), which mainly focuses on the identification of faults [5].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Transformers Health Index Based on the Markov Chain

et al. 2017

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This paper presents a study on the application of the Markov Model (MM) to determine the transformer population states based on Health Index (HI). In total, 3195 oil samples from 373 transformers ranging in age from 1 to 25 years were analyzed. First, the HI of transformers was computed based on yearly individual oil condition monitoring data that consisted of oil quality, dissolved gases, and furanic compounds. Next, the average HI for each age was computed and the transition probabilities were obtained based on a nonlinear optimization technique. Finally, the future deterioration performance curve of the transformers was determined based on the MM chain algorithm. It was found that the MM can be used to predict the future transformers condition states. The chi-squared goodness-of-fit analysis revealed that the predicted HI for the transformer population obtained based on MM agrees with the average computed HI along the years, and the average error is 3.59%.

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Section: Discussionmentioning

confidence: 99%

Section: Condition Assessment and Health Index (Hi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Transformers Health Index Based on the Markov Chain

et al. 2017

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“…The key gas is hydrogen (H 2 ), which indicates the problem involving the electrical corona that the arcing in oil respectively. The condition evaluation of power transformer was performed by using the Dissolved Gas Analysis method due to the readiness of data and as well-know practical experience [11]. The two sample models of power transformer 115/22 kV, 25MVA rated was presented to illustrate the problem of arcing in oil, corona in oil involving the cellulose insulation and overheated cellulose respectively.…”

Section: B the Second Transformermentioning

confidence: 99%

A prediction technique of power transformer condition assessment via DGA parameters

Haema

Phadungthin

2013

2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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This dissolved gas analysis is the most important tool for determining the condition of power transformer. It is the first indicator of a problem and can identify deterioration of the insulating such as overheating, hot spot, partial discharge and arcing. Therefore, this paper presents a prediction technique of power transformer condition assessment, by using DGA parameters in the analysis. The key gas parameters include hydrogen (H 2 ), methane (CH 4 ), acetylene (C 2 H 2 ), ethane (C 2 H 6 ), carbon monoxide (CO) and carbon dioxide (CO 2 ). The historical test results of the dissolved gas analysis of power transformers rated 115/22 kV 50 MVA analyzed as an example. There are two procedures used in the condition assessment; one is total dissolved combustible gas while the other is key gas. Firstly, the total dissolved combustible gas is considered. Next, the key gas is utilized for specify the incipient faults. Finally, the known condition can be used to plan appropriate maintenance effectively in the utility. This results in higher system reliability and longer useful lifetime of the power transformer.

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Regression Approach to Power Transformer Health Assessment Using Health Index

Sharma

2020

Lecture Notes in Mechanical Engineering

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Condition assessment of the health index for power transformer

Cited by 32 publications

References 1 publication

Estimation of Transformers Health Index Based on the Markov Chain

Estimation of Transformers Health Index Based on the Markov Chain

A prediction technique of power transformer condition assessment via DGA parameters

Regression Approach to Power Transformer Health Assessment Using Health Index

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