Proposed heptagon graph for DGA interpretation of oil transformers

Gouda, Osama E.; El-Hoshy, Salah H.; El-Tamaly, H.H.

doi:10.1049/iet-gtd.2017.0826

Cited by 48 publications

(27 citation statements)

References 21 publications

(31 reference statements)

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“…In the case verification, this method effectively solves the shortcomings of the Davul triangle method, which is difficult to diagnose low temperature overheating and partial discharge faults [16]. Besides, Gouda has drawn a heptagonal visual graphics for fault diagnose based on hydrocarbon gas and carbon oxides [17]. Moodley drawn a triangular graphics for MIO low energy degradation with 42 CH,H,CO gases data [18].…”

Section: Statistical Methods For Dga Interpretationmentioning

confidence: 99%

Improved DGA Methods of Power Transformer Fault Diagnosis: A Review

Ge¹,

Cui²,

Huo³

et al. 2018

Proceedings of the 2018 7th International Conference on Energy and Environmental Protection (ICEEP 2018)

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Power transformers are key components in the stable operation of electric power system. Reasonable arrangements of maintenance strategy according to transformers' working condition can decrease the loss lead by transformers' faults. The dissolved gas analysis (DGA) is the widest technique used in transformers' fault diagnosis and condition monitoring. Based on the conception of DGA technique, researchers and engineers have developed many methods and standard for faults diagnosis, such as IEC standard code, IEEE ratio, and Duval triangle method. They are practical and easy to use, but still face some problems. Many improvements for DGA have been carried out for improving the diagnostic accuracy. Artificial Intelligence (AI) method, statistics method or new diagnostic ways are the hot field for research. This paper has introduced the improved DGA methods of power transformer fault diagnosis in recent years and put forward some technical outlook of this field

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Section: Statistical Methods For Dga Interpretationmentioning

confidence: 99%

Improved DGA Methods of Power Transformer Fault Diagnosis: A Review

Ge¹,

Cui²,

Huo³

et al. 2018

Proceedings of the 2018 7th International Conference on Energy and Environmental Protection (ICEEP 2018)

View full text Add to dashboard Cite

show abstract

“…During normal operation of the transformer, due to aging and cracking of insulation oil and solid insulation, a very small amount of gas will be decomposed, mainly including hydrogen (H 2 ), methane (CH 4 ), ethane (C 2 H 6 ), ethylene (C 2 H 4 ), acetylene (C 2 H 2 ), carbon monoxide (CO), carbon dioxide (CO 2 ), etc. [2][3][4]. When a fault or abnormality occurs inside the transformer, the contents of some components in these gases will increase rapidly.…”

Section: Parameter Selectionmentioning

confidence: 99%

“…During the long operation of the transformer, due to equipment aging, discharge fault, thermal fault, and other reasons, a small amount of gas will be produced in the insulation oil, and the content of various components of dissolved gas and the proportion of components in the oil are closely related to the operation condition of the transformer. Through dissolved gas analysis (DGA) [1][2][3][4][5], some latent faults in the transformer and their development degree can be found. DGA is an internationally recognized and effective method of diagnosing early transformer faults that has been proven in practice by many fault diagnoses.…”

Section: Introductionmentioning

confidence: 99%

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

et al. 2020

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Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

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“…Since cellulose degradation is specific in its gas generation, it can be dealt separately by diagnostic methods. Many conventional methods are proposed to identify the transformer faults based on DGA such as key gas method [1], ratio methods [5]- [7], and graphical methods [8]- [12]. Some of these methods were targeting diagnosis of main fault types in transformer oil and others were developed to include cellulose degradation.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements

2020

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The transformer fault diagnosis based on dissolved gas analysis is greatly affected by the uncertainties existing in measured data during oil sampling, handling and storage. This work aims to develop an efficient code matrix based on dissolved gas percentages for accurate fault diagnosis considering measurement uncertainties. Fuzzy system is utilized to produce the rules that map the limits of gas ratios for different fault types. Each gas percentage range is divided into three regions represented by three fuzzy memberships. The fuzzy system is then developed to relate the gas percentages to the fault type. The membership limits are then optimized by using heuristic algorithms in order to maximize the accuracy. Hybrid Grey Wolf Optimization (HGWO) algorithm is utilized to produce the diagnostic code matrix. The proposed code limits the impact of measurement uncertainties on the output fault diagnosis. Different levels of measurement uncertainties up to 20% are considered to validate the effectiveness of the new code in improving the diagnostic accuracy. The accuracies during the training and testing processes attained 97.45% and 95.45%, respectively, with the maximum uncertainty level of 20%. Moreover, randomly selected case studies representing various fault types are investigated using the proposed method. For each case study, various levels of uncertainties are imposed on the original data. The proposed method proved its easiness towards diagnosing transformer faults and robustness against measurement uncertainties.

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Proposed heptagon graph for DGA interpretation of oil transformers

Cited by 48 publications

References 21 publications

Improved DGA Methods of Power Transformer Fault Diagnosis: A Review

Improved DGA Methods of Power Transformer Fault Diagnosis: A Review

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements

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