Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Cheng, Lefeng; Yu, Tao

doi:10.20944/preprints201804.0109.v2

Cited by 12 publications

(5 citation statements)

References 80 publications

(123 reference statements)

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“…Hydrocarbon gases, such as methane and ethylene, are formed if the fault involves an oil-impregnated structure [7].…”

Section: Relationship Between Faults and Dissolved Gas Producedmentioning

confidence: 99%

“…Large quantities of carbon dioxide and carbon monoxide are produced when thermal faults attack cellulose. Hydrocarbon gases, such as methane and ethylene, are formed if the fault involves an oil‐impregnated structure [7].…”

Section: Fault Types and Dgamentioning

confidence: 99%

“…Once the gases have been identified and quantified, the result still needs to be interpreted to assess the condition of the transformer. Several methods have been proposed in the literature to predict the occurrence of faults and to determine their types by interpreting the concentration of the gases detected [7]. Several standards from different committees and organizations, such as International Electrotechnical Commission (IEC) 60559‐1999, Institute of Electrical and Electronics Engineers (IEEE) C57.104‐1991, and International Council on Large Electric Systems (CIGRE) TF 15.01.01 provide guidelines for DGA interpretation.…”

Section: Introductionmentioning

confidence: 99%

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Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

Nanfak

Eke

Kom

et al. 2021

IET Generation Trans & Dist

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In this contribution, a new dissolved gas analysis (DGA) method combining key gases and ratio approaches for power transformer fault diagnostic is presented. It is based on studying subsets and uses the five main hydrocarbon gases including hydrogen (H 2 ), methane (CH 4 ), ethane (C 2 H 6 ), ethylene (C 2 H 4 ), and acetylene (C 2 H 2 ). The proposed method uses 475 samples from the dataset divided into subsets formed from the maximum and minimum(s) concentrations of the whole dataset. It has been tested on 117 DGA sample data and validated on the International Electrotechnical Commission (IEC) TC10 database. The performance of the proposed diagnostic method was evaluated and compared with the following diagnostic methods: IEC ratios method, Duval's triangle (DT), three ratios technique (TRT), Gouda's triangle (GT), and self-organizing map (SOM) clusters. The results found were analysed by computer simulations using MATLAB software. The proposed method has a diagnosis accuracy of 97.42% for fault types, as compared to 93.16% of TRT, 96.58% of GT method, 97.25% of SOM clusters method and 98.29% of DT method. However, in terms of fault severity, the proposed method has a diagnostic accuracy of 90.59% as compared to 78.90% of SOM clusters method, 83.76% of TRT, 88.03% of DT method, and 89.74% of GT method.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Hydrocarbon gases, such as methane and ethylene, are formed if the fault involves an oil-impregnated structure [7].…”

Section: Relationship Between Faults and Dissolved Gas Producedmentioning

confidence: 99%

Section: Fault Types and Dgamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

Nanfak

Eke

Kom

et al. 2021

IET Generation Trans & Dist

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“…4. According to GB/T 7252-2001 [28], the standard value of the onboard traction transformer C 2 H 4 in the normal state should be zero ppm. In Table 4, the content of C 2 H 4 significantly exceeds the standard, which further reflects the status change of the onboard traction transformer.…”

Section: ) Characteristics Of Dissolved Gases In Insulating Oilmentioning

confidence: 99%

Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering

Zhu¹,

Dong

2021

IEEE Access

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The onboard traction transformer is a critical equipment of high-speed trains, its running state directly affects the safety and stability of a train's operation. Given the complexity of the running condition of the onboard traction transformer, this paper proposes a running state diagnosis algorithm based on kernel principal component analysis (KPCA) and fuzzy clustering. To fully extract the status information of the onboard traction transformer, the aging characteristics of insulating oil and main insulation are analyzed under different running mileage as the first step. Thereby, to eliminate the signal redundancy, the status feature set of the onboard traction transformer is analyzed by KPCA combined with the characteristic quantities of the traditional dissolved gas analysis (DGA), and the eigenvalues with the contribution rate of over 95% are used as new eigenvectors. Finally, a status diagnosis model is established by using fuzzy clustering analysis, considering the limitations of fault data of onboard traction transformer. The results from field collected data show that the proposed method is effective in diagnosing the running status of the onboard traction transformer.INDEX TERMS Onboard traction transformer; running status diagnosis; insulation aging; kernel principal component analysis; fuzzy clustering

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“…It is demonstrated that fuzzy logic, metaheuristics, and ANN can provide improved performance in general engineering applications [ 19 , 20 , 21 , 22 , 23 ]. Specifically in transformer oil diagnoses, [ 24 ] introduces an intelligent approach used to diagnose the fault and to make the decision for oil-immersed power transformers, based on the DGA principle. In [ 25 ], a deep parallel detection technique for dissolved gas analysis of the transformer is used.…”

Section: Introductionmentioning

confidence: 99%

Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

Ward

Elfaraskoury

Badawi

et al. 2021

Sensors

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Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.

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Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Cited by 12 publications

References 80 publications

Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering

Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

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