Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM

Luo, Zhao; Zhang, Zhiyuan; Xu, Yan; Qin, Jinghui; Zhu, Zhendong; Wang, Hao; Gao, Zeyong

doi:10.1155/2020/2420456

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…The composition of the greatest individual gas amount determines whether the transformer will fail using the key gas approach. Four categories of failures in transformers using the key gas approach are recognized: electrical partial discharge, electrical arcing, thermal oil, and thermal cellulose [15], [16].…”

Section: Key Gas Methodsmentioning

confidence: 99%

Analysis of interference methods on transformers based on the results of dissolved gas analysis tests

Siregar

Lumbanraja

2023

IJECE

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<span lang="EN-US">In the operation of the power transformer, several maintenance efforts must be made to ensure the condition of the transformer is in good condition. The problems that usually arise are a thermal failure and electrical failure. The use of insulating media such as transformer oil and transformer insulation paper can be disrupted by this failure. Dissolved gas analysis, which identifies the types and concentrations of dissolved gas in transformer oil, can reveal details on fault indicators in power transformers (DGA). In this study, we used the interpretation of the IEEE std 2008-C57.104 (total dissolved combustible gas (TDCG), key gas, Rogers ratio method), the interpretation of IEC 2015-60599 (Duval triangle and basic gas ratio method), and the IEEE Std 2019-C57.104 interpretation (Duval pentagon method). The outcome of the DGA test is used to determine the conditions and indications of disturbances in the transformer for power. Using various gas analysis techniques also impacts the outcome of the fault indication. This variation has affected the types of gas used in the computation and the gas concentration limit value estimation. After the gas analysis, it was found that the oil purification process was also proven to reduce the concentration of combustible gases.</span>

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Section: Key Gas Methodsmentioning

confidence: 99%

Analysis of interference methods on transformers based on the results of dissolved gas analysis tests

Siregar

Lumbanraja

2023

IJECE

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“…In order to track the dissolved gas concentration over time, researchers have adapted methods that can analyze time series. A new LSTM model (SDAE-LSTM) is proposed (Luo et al, 2020) to identify and parametrically analyze dissolved gases in the insulating oil of power transformers. SDAE has strong ability of mining the internal features of data and anti-interference ability.…”

Section: Neural Networkmentioning

confidence: 99%

“…Intelligent techniques help to resolve the uncertainty of traditional DGA methods due to boundary problems and unresolved codes or multi-fault scenarios (Wani et al, 2021). Researchers have applied many artificial intelligence techniques to DGA fault diagnosis, such as neural networks (Duan and Liu, 2011;Wang et al, 2016;Qi et al, 2019;Yan et al, 2019;Yang et al, 2019Yang et al, , 2020Luo et al, 2020;Velásquez and Lara, 2020;Mi et al, 2021;Taha et al, 2021;Zhou et al, 2021), support vector machine (SVM) (Wang and Zhang, 2017;Fang et al, 2018;Huang et al, 2018;Illias and Liang, 2018;Kari et al, 2018;Kim et al, 2019;Zeng et al, 2019;Zhang et al, 2019;Zhang Y. et al, 2020;Benmahamed et al, 2021), and clustering (Islam et al, 2017;Misbahulmunir et al, 2020). These techniques involve statistical machine learning, deep learning, etc.…”

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confidence: 99%

Fault diagnosis of transformer using artificial intelligence: A review

et al. 2022

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Transformer is one of the important components of the power system, capable of transmitting and distributing the electricity generated by renewable energy sources. Dissolved Gas Analysis (DGA) is one of the effective techniques to diagnose early faults in oil-immersed transformers. It correlates the concentration and ratio of dissolved gases with transformer faults. Researchers have proposed many methods for fault diagnosis, such as double ratio method, Rogers method, Duval triangle method, etc., but all of them have some problems. Based on the strong data mining capability and good robustness of AI techniques, many researchers introduced AI techniques to mine the features of DGA data. According to the characteristics and scale of DGA data, researchers select appropriate AI techniques or make appropriate improvements to AI techniques to improve diagnostic performance. This paper presents a systematic review of the literature on the application of artificial intelligence techniques for DGA-based diagnosis and for solving intractable problems in early transformer fault diagnosis, which include neural networks, clustering, support vector machines, etc. In addition to reviewing the applications of these intelligent techniques, the diagnostic thinking proposed in this literature, such as the introduction of temporal parameters for comprehensive analysis of DGA data and the extraction of optimal features for DGA data, is also reviewed. Finally, this paper summarizes and prospects the artificial intelligence techniques applied by researchers in transformer fault diagnosis.

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“…The performance accuracy ratio (PR) is used to measure fault diagnostic accuracy [23]. The criteria for the overall performance of the model is defined as the ratio of the number of equal predictions (n) to the total number of predictions (N) and is given by: 𝑃𝑅 = 𝑛/𝑁 (7) Validation accuracy (VA) = 𝑃𝑅 * 100%…”

Section: Accuracy Of Fault Diagnosis Evaluationmentioning

confidence: 99%

A long short-term memory based prediction model for transformer fault diagnosis using dissolved gas analysis with digital twin technology

Sarma¹,

Reddy

Nirgude

et al. 2022

IJPEDS

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<span lang="EN-US">The most significant tool for defect diagnostics in transformers is dissolved gas analysis (DGA). The time series prediction of dissolved gas levels in oil, when combined with dissolved gas analysis, provides a foundation for transformer fault diagnosis and an early warning. A long short-term memory (LSTM) based prediction model is developed in this paper to train the digital twin for identifying the essential fault in the transformer via DGA. The model is fed with three different gas concentrations as input. This study achieves the performance evaluation in terms of validation accuracy. The suggested model exhibits significant validation accuracy of 99.83%, as indicated by the analyses, thus the early prediction of transformer maintenance is aided. It can be validated that the LSTM model for fault identification and analysis using dissolved gas in the transformer has a lot of research potential.</span>

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Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM

Cited by 12 publications

References 21 publications

Analysis of interference methods on transformers based on the results of dissolved gas analysis tests

Analysis of interference methods on transformers based on the results of dissolved gas analysis tests

Fault diagnosis of transformer using artificial intelligence: A review

A long short-term memory based prediction model for transformer fault diagnosis using dissolved gas analysis with digital twin technology

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