A novel two‐stage Dissolved Gas Analysis fault diagnosis system based semi‐supervised learning

Tan, Xuemin; Guo, Chuangxin; Wang, Ke; Wan, Fu

doi:10.1049/hve2.12195

Cited by 23 publications

(11 citation statements)

References 29 publications

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“…Generally, dissolved gas analysis (DGA) is used to analyse gases in transformer oil [16,17]. It is one of the most important and effective techniques for assessing the condition of oil-filled transformers [18].…”

Section: Introductionmentioning

confidence: 99%

Dissolved gas analysis and dissipation factor measurement of mineral oil‐based nanofluids under thermal and electrical faults

et al. 2023

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Mineral oil is the most frequent insulating liquid used in oil‐immersed transformers for electrical insulation and heat dissipation. However, oil‐based nanofluids are becoming more popular in scientific research as they have proved to have better dielectric and thermal characteristics. When applying these nanofluids into actual transformers, they would be exposed to thermal and electrical stresses. Thus, The aim of the authors is to investigate the generation pattern of dissolved gases in nanofluids under thermal and electrical faults and to assess the applicability of traditional Dissolved Gas Analysis (DGA) techniques if oil‐based nanofluids are used in transformers. Oil‐based nanofluid samples were prepared using a magnetic stirrer and an ultrasonic homogeniser and then subjected to simulated thermal and electrical faults in the laboratory using properly sealed test cells. Three types of metal oxides, Silicon dioxide, Titanium dioxide, and Aluminium oxide nanoparticles, have been used to prepare nanofluids with 0.02 g/L and 0.04 g/L concentrations. The gases released and dissolved into oil due to the simulated faults were analysed and compared to traditional mineral oil as a benchmark. The dielectric dissipation factor was obtained and analysed for all samples. According to the findings, the presence and concentration of nanoparticles were shown to influence the amount of gases produced. As a result, this research is crucial in guaranteeing that traditional DGA techniques can be employed in transformers that use oil‐based nanofluids.

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Section: Introductionmentioning

confidence: 99%

Dissolved gas analysis and dissipation factor measurement of mineral oil‐based nanofluids under thermal and electrical faults

et al. 2023

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“…Dissolved gas analysis (DGA) is one of the most common and reliable fault diagnosis methods of oil-immersed power equipment [10,11]. Gas generation in insulating oil needs to be examined regularly to anticipate and diagnose the presence of faults.…”

Section: Introductionmentioning

confidence: 99%

Difference analysis of dissolved gas in natural ester insulating fluids under typical electrical and thermal faults: An experimental study

Xing

et al. 2023

High Voltage

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Natural ester insulating oil is widely used in transformers due to its good fire resistance and biodegradability. It is mainly composed of triglycerides, and there are some differences in fatty acid composition and content among natural esters. Therefore, the fault diagnosis methods based on dissolved gas analysis are different. Three kinds of natural ester insulating oils are used to study the dissolved gas in oil under typical electrical and thermal faults of power transformers. Thermal faults from 200°C to 800°C and electrical faults with different discharge energies, which include partial discharge, breakdown discharge, and arc discharge, are simulated. The characteristic gases and their variation trends of natural esters under thermal and electrical faults are obtained through the experimental results. The gas generation characteristics of the three natural ester insulating oils under typical electrical and thermal faults are almost similar, but the relative percentages of characteristic gases have certain differences. Natural ester insulating oil with a higher unsaturation degree tends to produce more H2 and less C2H4. Finally, the fault diagnosis methods are used to determine the fault gas data, and the Duval pentagon is modified and improved according to the gas generation characteristics of natural esters.

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“…For example, Yang et al [10] proposed a novel SSL method based on a double-stacked autoencoder for power transformer fault diagnosis. Tan et al [11] proposes a twostage semi-supervised transformer fault diagnosis system based on improved support vector machine. However, these SSL methods only learn fault features from the input in Euclidean space and do not fully utilize the local geometry property between all samples, thus making it difficult to mine potential associations among samples.…”

Section: Introductionmentioning

confidence: 99%

A novel semi-supervised learning method for power transformer fault diagnosis with limited labeled data

Zhou¹,

Wang²,

Tian³

et al. 2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Identifying power transformer faults accurately is critical to maintaining the stable operation of power system. Intelligent fault diagnosis algorithms based on dissolved gases have been extensively researched and implemented. However, in practice, collecting labeled data is time-consuming and costly. Therefore, it is necessary to establish a valid diagnostic model with limited labeled data. To solve this problem, a novel semi-supervised learning method for power transformer fault diagnosis is proposed in this paper. First, all the dissolved gas samples are constructed as a weighted K-nearest neighbor (KNN) graph to initially describe association among all samples. Then, a semi-supervised random multireceptive field propagation graph convolutional network (SSRMFPGCN) is designed for fault feature extraction and classification. Finally, the collected power transformer fault data are used to validate the proposed method. The experimental results show that the method proposed in this paper can still achieve 94.06% accuracy with only 20% of labeled training samples, which is significantly superior to the traditional intelligent diagnosis methods.

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A novel two‐stage Dissolved Gas Analysis fault diagnosis system based semi‐supervised learning

Cited by 23 publications

References 29 publications

Dissolved gas analysis and dissipation factor measurement of mineral oil‐based nanofluids under thermal and electrical faults

Dissolved gas analysis and dissipation factor measurement of mineral oil‐based nanofluids under thermal and electrical faults

Difference analysis of dissolved gas in natural ester insulating fluids under typical electrical and thermal faults: An experimental study

A novel semi-supervised learning method for power transformer fault diagnosis with limited labeled data

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