Study on Correlation Among Oil Dielectric Characteristics, Dissolved Gases, and Operating Life of 150 kV Power Transformer

Diwyacitta, Karunika; Prasojo, Rahman Azis; Suwarno, Suwarno

doi:10.15676/ijeei.2017.9.3.12

Cited by 15 publications

(3 citation statements)

References 20 publications

(36 reference statements)

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“…From oil characteristics point of view, almost all healthy-classified transformers have relatively good oil parameters. This is in line with study in [42], that the ageing process happens during the life of transformer, decrease the condition of the transformer and changes certain parameters in oil insulation.…”

Section: F Application Of the Developed Model To In-service Transforsupporting

confidence: 89%

Power Transformer Paper Insulation Assessment based on Oil Measurement Data using SVM-Classifier

Prasojo¹,

Suwarno²

2018

IJEEI

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Oil immersed paper insulation condition is a crucial aspect of power transformer's life condition diagnostics. The measurements testing database collected over the years made it possible for researchers to implement classification analysis to in-service power transformer. In order to generate a reliable model, more studies related to machine learning implementation to power transformer assessment need to be done. In this article, the objective of the study is to develop reliable new approach in transformer oil-immersed paper insulation condition assessment based on SVM-classifier model using its oil measurements. The measurements data (dielectric characteristics, dissolved gas analysis, and furanic compounds) of 149 transformers with primary voltage of 150 kV had been gathered and analyzed. The algorithm employed for developing classification model is Support Vector Machine (SVM). The model had been trained and tested using different datasets. Several different models have been created and the best has been chosen, resulting in 90.63% accuracy in predicting the oil-immersed paper insulation condition. Further implementation was executed to classify oil-paper condition of 19 transformers which Furan data is not available. The classification results were combined, reviewed, and compared to conventional assessment methods and standards. The comparation confirmed that the model developed has the ability to do classification of current oil-paper condition for the transformer population observed, based on Dissolved Gasses and Dielectric Characteristics.

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Section: F Application Of the Developed Model To In-service Transforsupporting

confidence: 89%

Power Transformer Paper Insulation Assessment based on Oil Measurement Data using SVM-Classifier

Prasojo¹,

Suwarno²

2018

IJEEI

Self Cite

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show abstract

“…From oil characteristics point of view, almost all healthy-classified transformer have relatively good oil parameters. This is in line with study in [35], that the ageing process happens during the life of transformer, decrease the condition of the transformer and changes certain parameters in oil insulation.…”

Section: Application Of the Model Developedsupporting

confidence: 89%

Power Transformer Insulation Assessment Based on Oil-Paper Measurement Data Using SVM-Classifier

Suwarno¹,

Prasojo²

2018

Preprint

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Oil immersed paper insulation condition is a crucial aspect of power transformer’s life condition diagnostic. The measurement testing database collected over the years made it possible for researchers to implement classification analysis to in-service power transformer. This article presents classification analysis of transformer oil-immersed paper insulation condition. The measurements data (dielectric characteristics, dissolved gas analysis, and furanic compounds) of 149 transformers with primary voltage of 150 kV had been gathered and analyzed. The algorithm used for developing classification model is Support Vector Machine (SVM). The model has been trained and tested using different datasets. Different models have been created and the best chosen, resulting in 90.63% accuracy in predicting the oil-immersed paper insulation condition. Further implementation was executed to classify oil-paper condition of 19 Transformers which Furan data is not available. The classification results combined, reviewed, and compared to conventional assessment methods and standards, confirming that the model developed has the ability to do classification of current oil-paper condition based on Dissolved Gasses and Dielectric Characteristics.

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“…Nevertheless, Paper Condition Factor still can be determined. Paper insulation condition can be evaluated by an approach using other transformer parameters [27,[30][31][32]. In this calculation, paper condition factor determination only uses the available parameter.…”

Section: Study Casementioning

confidence: 99%

High voltage power transformer condition assessment considering the health index value and its decreasing rate

2021

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A health index method is a useful tool for the transformer assessment condition. This method has been used in several previous studies. However, most of them have not observed the health index decreasing rate as an aspect to improve the transformer condition assessment. A health index method with different approaches is proposed, considering the values and decreasing rate to assess the transformer condition. Inspection data from in‐service and out‐of‐service 150 kV power transformers provided by the Indonesian electric company are included. The correlation between operating age and Health Index value and Health Index decreasing rate was also observed. With increasing operation age, the Health Index value tends to decrease with a correlation coefficient R 2 of 0.631. Further analysis was conducted to power transformers with historical data of 3 years or more, which showed that the tendency of the Health Index value decreasing rate is higher with older transformers. This paper also illustrates the Health Index analysis of 35 out‐of‐service transformers, resulting in a more suitable Health Index value compared to the previous approach. This study proposes the transformer risk assessment based on its health index value and the decreasing rate.

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Study on Correlation Among Oil Dielectric Characteristics, Dissolved Gases, and Operating Life of 150 kV Power Transformer

Cited by 15 publications

References 20 publications

Power Transformer Paper Insulation Assessment based on Oil Measurement Data using SVM-Classifier

Power Transformer Paper Insulation Assessment based on Oil Measurement Data using SVM-Classifier

Power Transformer Insulation Assessment Based on Oil-Paper Measurement Data Using SVM-Classifier

High voltage power transformer condition assessment considering the health index value and its decreasing rate

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