Application of Duval Pentagon Compared with Other DGA Interpretation Techniques: Case Studies for Actual Transformer Inspections Including Experience from Power Plants in Thailand

Pattanadech, Norasage; Wattakapaiboon, W.

doi:10.1109/iceast.2019.8802523

Cited by 26 publications

(15 citation statements)

References 11 publications

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“…This stage guarantees better results in fault identification than stages 1 and 2. With the Duval method in the FDIp, a higher level of reliability in the detection of failures is obtained, guaranteeing a more effective diagnosis in terms of the detection of incipient failures [36]. Although a greater identification of the failures is achieved at this stage, it has the drawback that it does not guarantee the Identification of DGA cases free of failures.…”

Section: Ii3 3rd Stage Classification Based On the Duval Methodsmentioning

confidence: 99%

“…For the DPM application, the FDIp system uses the vertices H2:(0, 40), C2H6: (−38, 12.4), CH4: (−23.5, −32.4), C2H4: (23.5, −32.4) y C2H2: (38, 12.4) y represents the coordinates of each fault zone below [35]: -PD: (0, 24.5); (0, 33); (−1, 24.5); (−1, 33); -D1: (0, 40); (38, 12); (32, −6); (4, 16); (0, 1.5); -D2: (4, 16); (32, −6); (24, −30); (−1, −2); -T1: (−22.5, −32); (−6, −4); (−1, −2); (0, 1.5); (−35, 3); -T2: (1, −32); (−6, −4); (−22.5, −32); -T3: (24, −30); (−1, −2); (−6, −4); (1, −32); -S: (−35, 3); (0, 1.5); (0, 24.5); (0, 33); (-1, 24.5); -(−1, 33); (0, 40).This stage guarantees better results in fault identification than stages 1 and 2. With the Duval method in the FDIp, a higher level of reliability in the detection of failures is obtained, guaranteeing a more effective diagnosis in terms of the detection of incipient failures[36]. Although a greater identification of the failures is achieved at this stage, it has the drawback that it does not guarantee the Identification of DGA cases free of failures.…”

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confidence: 99%

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A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

Fernández¹,

Corrales-Barrios

Benítez-Pina

et al. 2022

IREE

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The availability of power transformers is essential for the safety and continuity of electrical service. Today's fault diagnosis methods use intelligent techniques such as neural networks, support machines, hybrid techniques, among others. Although they present good results, these techniques find restrictions in the ability to determine the precise moment in the event of multiple and small-magnitude faults. The proposal includes a new algorithm based on fuzzy rules that incorporates the daily increase of dissolved gases in the transformer oil that improves the classification of incipient faults. With reliable samples of gas dissolved in oil, the method proposed in the research can obtain a total precision rate of 91.4%. In contrast, this degree of precision is lower in other conventional methods reported in the bibliography. In addition, its performance in the classification of multiple failures is 97.5%. The method uses fuzzy logic tools to suggest actions aimed at preventive maintenance by monitoring the total of combustible gases dissolved in the oil. The proposal is a simple and easy solution to implement in practice that allows determining the status of the transformer in service without affecting the continuity of the electricity supply.

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Section: Ii3 3rd Stage Classification Based On the Duval Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

Fernández¹,

Corrales-Barrios

Benítez-Pina

et al. 2022

IREE

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show abstract

“…Several studies [48,49,50,51,52,53,54,55,56] indicate that the best method for the faults identification in the transformer insulation is the Duval triangle method (DTM).The Duval pentagon method (DPM) was created to improve DTM results. Several recent studies [48,49] indicate that the DPM success rate improves those of the DTM, since the DPM allows to identify the normal aging of the transformer insulation. Although the DPM is shown as the best method of identifying faults, it should be noted that it is a fairly new method that should be studied further to improve its validity.…”

Section: Dissolved Gas-in-oil Analysismentioning

confidence: 99%

Dissolved Gas Analysis Equipment for Online Monitoring of Transformer Oil: A Review

Bustamante

Canteli

Arroyo

et al. 2019

Sensors

116

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Power transformers are the most important assets of electric power substations. The reliability in the operation of electric power transmission and distribution is due to the correct operation and maintenance of power transformers. The parameters that are most used to assess the health status of power transformers are dissolved gas analysis (DGA), oil quality analysis (OQA) and content of furfuraldehydes (FFA) in oil. The parameter that currently allows for simple online monitoring in an energized transformer is the DGA. Although most of the DGA continues to be done in the laboratory, the trend is online DGA monitoring, since it allows for detection or diagnosis of the faults throughout the life of the power transformers. This study presents a review of the main DGA monitors, single- or multi-gas, their most important specifications, accuracy, repeatability and measurement range, the types of installation, valve or closed loop, and number of analogue inputs and outputs. This review shows the differences between the main existing DGA monitors and aims to help in the selection of the most suitable DGA monitoring approach according to the needs of each case.

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“…This complex combination is due to the following factors: − The DGA method is the most informative among the methods of early detection of defects in oil-filled transformer equipment. It allows diagnosing more than 70% of faults in the active part of the PT without disconnecting and removing the voltage [18]. − The Key Gas method provides the function of pre-filtering DGA tests for the presence/absence of signs of developing defects.…”

Section: Introductionmentioning

confidence: 99%

Predicting the technical condition of the power transformer using fuzzy logic and dissolved gas analysis method

Levin

Yahya

Boyarova

2022

IJECE

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Power transformers are one of the most important and complex parts of an electric power system. Maintenance is performed for this responsible part based on the technical condition of the transformer using a predictive approach. The technical condition of the power transformer can be diagnosed using a range of different diagnostic methods, for example, analysis of dissolved gases (DGA), partial discharge monitoring, vibration monitoring, and moisture monitoring. In this paper, the authors present a digital model for predicting the technical condition of a power transformer and determining the type of defect and its cause in the event of defect detection. The predictive digital model is developed using the programming environment in LabVIEW and is based on the fuzzy logic approach to the DGA method, interpreted by the key gas method and the Dornenburg ratio method. The developed digital model is verified on a set of 110 kV and 220 kV transformers of one of the sections of the distribution network and thermal power plant in the Russian Federation. The results obtained showed its high efficiency in predicting faults and the possibility of using it as an effective computing tool to facilitate the work of the operating personnel of power enterprises.

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Application of Duval Pentagon Compared with Other DGA Interpretation Techniques: Case Studies for Actual Transformer Inspections Including Experience from Power Plants in Thailand

Cited by 26 publications

References 11 publications

A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

Dissolved Gas Analysis Equipment for Online Monitoring of Transformer Oil: A Review

Predicting the technical condition of the power transformer using fuzzy logic and dissolved gas analysis method

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