Artificial Neural Network Application in Estimation of Dissolved Gases in Insulating Mineral Oil from Physical-Chemical Datas for Incipient Fault Diagnosis

Barbosa, Fábio Rocha; Almeida, Otacílio M.; Braga, Arthur P. S.; Tavares, Cicero M.; Amora, Márcio André Baima; Aragao, Francisco A. P.; Braga, Paulo R. O.; Lima, Swiany Silveira

doi:10.1109/isap.2009.5352919

Cited by 4 publications

(2 citation statements)

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“…In [14], DGA and oil quality indices, along with paper insulation quality index, are classified and normalized in five groups, and a combination of fuzzy logic and support vector machine methods is used to determine the transformer health index. e DGA index is used to determine the faults that occurred in the transformer [15,16] and the oil quality index is obtained by the electrical, physical, and chemical oil parameters [12,13,15]. One of the common methods for calculating the DGA index for fault detection in transformers is artificial neural networks [16].…”

Section: Introductionmentioning

confidence: 99%

“…e DGA index is used to determine the faults that occurred in the transformer [15,16] and the oil quality index is obtained by the electrical, physical, and chemical oil parameters [12,13,15]. One of the common methods for calculating the DGA index for fault detection in transformers is artificial neural networks [16]. Fault detection, loading, and evaluation of transformer conditions are one of the essential tasks in the operation of transformers [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Interaction of Transformer Oil Parameters on Each Other and on Transformer Health Index Using Curve Estimation Regression Method

Saeid

Zeinoddini‐Meymand

Kamel

et al. 2022

International Transactions on Electrical Energy Systems

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Power transformers are one of the most significant and expensive equipment in power systems that are exposed to electrical, thermal, and chemical tensions. The transformer health index is a measure that uses test data and field inspections to assess the condition and determine the remaining life of the transformer. The purpose of this article as a new idea is to determine the relationships between electrical, physical, and chemical parameters of transformer oil, dissolved gases, and the transformer health index. One of the advantages of using the regression method in analyzing transformer data compared to the other methods to evaluate the transformer health index is determining the influence of the parameters that have the most impact on each other. Some achievements of this article are as follows: (1) introducing moisture content as the parameter that plays an effective role in reducing dielectric oil breakdown voltage and improving the transformer health index; (2) determining the inverse relationship between acidity and furfural components; (3) determining furfural as a parameter with the greatest role in reducing the Interfacial tension (IFT) of oil (molecular interconnection); (4) determining CO gas as the parameter with the most role in the production of furfural component; (5) determining C2H2 gas as the parameter with the most role in producing the acid component. For example, with a 1 ppm increase in the moisture component, the oil breakdown voltage decreases by 0.583 kV in the compound, growth, exponential, and logistic regressions, or with a 1 ppm increase in the furfural component, the oil interfacial tension decreases by 0.644 mN/m in power regression. In this article, the curve estimation regression method is used and the results are plotted by SPSS statistical software to analyze the interaction between different transformer parameters. To perform the simulations, test data related to 120 transformers have been considered.

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