Diagnosis of power transformer faults on fuzzy three-ratio method

Ma, Hongzhong; Li, Zheng; Ju, Ping; Han, Jingdong; Zhang, Limin

doi:10.1109/ipec.2005.206897

Cited by 24 publications

(15 citation statements)

References 7 publications

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“…The six methods are tested to interpret 92 data sets of 5 fault gases [1,[3][4][5]. These five key gases are H 2, CH 4 , C 2 H 6 , C 2 H 4 and C 2 H 2 .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Study and Analysis of DGA Methods for Transformer Mineral Oil

Muhamad

Phung

Blackburn

et al. 2007

2007 IEEE Lausanne Power Tech

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Dissolved gas-in-oil analysis (DGA) is a sensitive and reliable technique for the detection of incipient fault condition within oil-immersed transformers. The presence of certain key gases is monitored and quantified. There are a number of methods developed for analyzing these gases and interpreting their significance: Key Gas, Rogers Ratio, Doernenburg, Logarithmic Nomograph, IEC Ratio and Duval Triangle. This paper investigates the accuracy and consistency of these methods in interpreting the transformer condition. The evaluation is carried out on DGA data obtained from the local power utilities and from published papers. The data consists of 92 different cases. The key gases considered are hydrogen, methane, ethane, ethylene and acetylene. A MATLAB program was developed to automate the evaluation of the methods.

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“…The six methods are tested to interpret 92 data sets of 5 fault gases [1,[3][4][5]. These five key gases are H 2, CH 4 , C 2 H 6 , C 2 H 4 and C 2 H 2 .…”

Section: Methodsmentioning

confidence: 99%

“…Fuzzy Logic is known as one of the expert systems that can be used to diagnose the faults because of its ability in storing knowledge and using it to make decision [5]. Here, the final diagnosis rules are automatically determined and the membership functions of the corresponding fuzzy subsets are simultaneously adjusted.…”

Section: (4)mentioning

confidence: 99%

Comparative Study and Analysis of DGA Methods for Transformer Mineral Oil

Muhamad

Phung

Blackburn

et al. 2007

2007 IEEE Lausanne Power Tech

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“…To examine the robustness of the proposed model for DGA data identification and quantification, DGA results of pre known fault types from previously published research papers [12,22,[30][31][32] are tested using the proposed model shown in Fig. 8 to compare the model's output with the actual fault.…”

Section: Validation Of the Proposed Modelmentioning

confidence: 99%

“…Availability of DGA data history has recently motivated researchers to develop a standard approaches for DGA interpretation based on mathematical and artificial intelligent (AI) techniques [11][12][13][14][15][16][17][18][19][20]. The application of AI in the interpretation of DGA data are mainly to overcome the drawbacks arise from the application of ratio methods that include failure to identify fault types in case of multiple fault conditions and the invalid code that some DGA results may result in.…”

Section: Introductionmentioning

confidence: 99%

A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis

Abu-Siada

Hmood

2015

International Journal of Electrical Power & Energy Systems

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“…At present, the mathematical apparatus of fuzzy logic and neural networks is widely used to detect developing defects and to recognize their type both by domestic [1][2][3][4][5][6] and foreign [7][8][9][10][11] researchers. In most of the published works, international, national or departmental standards or techniques for interpreting the results of chromatographic analysis of dissolved gases in oil (CADG) are used as a decisive rule.…”

Section: Introductionmentioning

confidence: 99%

Development of Fuzzy Neural Network for the Interpretation of the Results of Dissolved in Oil Gases Analysis

Bondarenko¹,

Shutenko²

2017

Elektroteh. elektromeh.

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Purpose. The purpose of this paper is a diagnosis of power transformers on the basis of the results of the analysis of gases dissolved in oil. Methodology. To solve this problem a fuzzy neural network has been developed, tested and trained. Results. The analysis of neural network to recognize the possibility of developing defects at an early stage of their development, or growth of gas concentrations in the healthy transformers, made after the emergency actions on the part of electric networks is made. It has been established greatest difficulty in making a diagnosis on the criterion of the boundary gas concentrations, are the results of DGA obtained for the healthy transformers in which the concentration of gases dissolved in oil exceed their limit values, as well as defective transformers at an early stage development defects. The analysis showed that the accuracy of recognition of fuzzy neural networks has its limitations, which are determined by the peculiarities of the DGA method, used diagnostic features and the selected decision rule. Originality. Unlike similar studies in the training of the neural network, the membership functions of linguistic terms were chosen taking into account the functions gas concentrations density distribution transformers with various diagnoses, allowing to consider a particular gas content of oils that are typical of a leaky transformer, and the operating conditions of the equipment. Practical value. Developed fuzzy neural network allows to perform diagnostics of power transformers on the basis of the result of the analysis of gases dissolved in oil, with a high level of reliability. References 16, tables 3, figures 9. Key words: diagnostics of transformers, analysis of dissolved gases in oil, peculiarities of gas content, concentration levels, fuzzy neural networks, membership function, Weibull distribution, network training, fuzzy conclusion, wrong decisions.Разработана и обучена нечеткая нейронная сеть для интерпретации результатов хроматографического анализа растворенных в масле газов. Предложено определять функции принадлежности лингвистических термов с учетом функций плотностей распределения концентраций газов для трансформаторов с различным состоянием. Выполнено тестирование обученной сети на независимой выборке. Проанализированы возможности нейронных сетей распознавать развивающиеся дефекты на ранней стадии их развития, или рост концентраций газов в исправных трансформаторах, после аварийных воздействий со стороны электрических сетей. Библ. 16, табл. 3, рис. 9. Ключевые слова: диагностика трансформаторов, анализ растворенных в масле газов, особенности газосодержания, уровни концентраций, нечеткие нейронные сети, функции принадлежности, распределение Вейбулла, обучение сети, нечеткий вывод, ошибочные решения.

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Diagnosis of power transformer faults on fuzzy three-ratio method

Cited by 24 publications

References 7 publications

Comparative Study and Analysis of DGA Methods for Transformer Mineral Oil

Comparative Study and Analysis of DGA Methods for Transformer Mineral Oil

A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis

Development of Fuzzy Neural Network for the Interpretation of the Results of Dissolved in Oil Gases Analysis

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