Identification and Application of Machine Learning Algorithms for Transformer Dissolved Gas Analysis

Rao, U. Mohan; Fofana, I.; Rajesh, Kandala. N. V.P. S.; Picher, P.

doi:10.1109/tdei.2021.009770

Cited by 49 publications

(14 citation statements)

References 19 publications

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“…The dissolved gas data used in this case were obtained from the State Grid Corporation of China and published literature [21][22][23]. These samples relate to power transformer of each common voltage class and contain the concentrations of H 2 , CH 4 , C 2 H 4 , C 2 H 6 and C 2 H 2 in seven condition types: normal, thermal fault of low temperature (LT), thermal fault of medium temperature (MT), thermal fault of high temperature (HT), partial discharge (PD), low-energy discharge (LD), and high-energy discharge (HD).…”

Section: Data Description and Processingmentioning

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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Section: Data Description and Processingmentioning

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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“…Classes of inchoate faults that could be engrossed in a transformer are controlled by supervising and scrutinizing the concentration level, production rate, gas ratio, and total level of combustible gases in insulating oil. There are three modes of fault conditions that instigate emancipating of faulty dissolved gases: partial discharge, energy discharge, and thermal expulsion [6][7][8][9][10]. There are numerous chemical and electrical practices existing in supervising insulation state in oil-submerged transformers including Dissolved Gas Analysis (DGA) and Furan Analysis which reveal the Degree of Polymerization of the cellulose paper [11,12].…”

Section: Introductionmentioning

confidence: 99%

On the Application of Artificial Neural Network for Classification of Incipient Faults in Dissolved Gas Analysis of Power Transformers

Thango

2022

MAKE

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Oil-submerged transformer is one of the inherent instruments in the South African power system. Transformer malfunction or impairment may interpose the operation of the electric power distribution and transmission system, coupled with liability for high overhaul costs. Hence, recognition of inchoate faults in an oil-submerged transformer is indispensable and it has turned into an intriguing subject of interest by utility owners and transformer manufacturers. This work proposes a hybrid implementation of a multi-layer artificial neural network (MLANN) and IEC 60599:2022 gas ratio method in identifying inchoate faults in mineral oil-based submerged transformers by employing the dissolved gas analysis (DGA) method. DGA is a staunch practice to discover inchoate faults as it furnishes comprehensive information in examining the transformer state. In current work, MLANN was established to pigeonhole seven fault types of transformer states predicated on the three International Electrotechnical Commission (IEC) combustible gas ratios. The designs enmesh the development of numerous MLANN algorithms and picking networks with the optimum performance. The gas ratios are in accordance with the IEC 60599:2022 standard whilst an empirical databank comprised of 100 datasets was used in the training and testing activities. The designated MLANN design produces an overall correlation coefficient of 0.998 in the categorization of transformer state with reference to the combustible gas produced.

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“…The main method for diagnosing the power transformers' technical state is analysis of dissolved gases, which result from the oil degradation during power transformer operation. The ratio of certain gases' concentrations allows one to detect the type of equipment damage and its location [9][10][11][12][13]; 2. Power factor analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The widespread and profound development of diagnostic methods of oil-filled highvoltage equipment through data analysis emphasizes the interest of industry insiders in reliable and accurate methods for detecting faults and damage inside oil-filled highvoltage equipment. Nevertheless, it should be noted that-according to the methods effectiveness-from the point of view of damage detection, the most effective and sensitive is transformer oil analysis [5,[9][10][11][12][13]32], while other methods are primarily focused on identifying equipment damage-specific points, when the fact of such damage has already been identified.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics

et al. 2022

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This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects’ observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers’ technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F1-score, was estimated to be 83%.

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Identification and Application of Machine Learning Algorithms for Transformer Dissolved Gas Analysis

Cited by 49 publications

References 19 publications

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

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

On the Application of Artificial Neural Network for Classification of Incipient Faults in Dissolved Gas Analysis of Power Transformers

Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics

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