Comparative study between dorneneburg and rogers methods for transformer fault diagnosis based on dissolved gas analysis using Matlab Simulink Tools

Taha, Ibrahim B. M.; Zaini, Hatim Ghazi; Ghoneim, Sherif S. M.

doi:10.1109/cencon.2015.7409570

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…The transformer DGA data used in the experiments of this paper are referenced from literature [10][11][12][13] and include some transformer fault sample data collected from a 220kV substation in Hubei Province, China. The training samples are classified into seven categories: normal condition (NC), thermal fault of low temperature (LT), thermal fault of medium temperature (MT), high temperature fault (HT), partial discharge (PD), low energy discharge (LD), and high energy discharge (HD).…”

Section: Image Data Augmentation Methods Based On Ganmentioning

confidence: 99%

Transformer fault diagnosis method based on Gramian angular field and graph convolution network

zhang,

Yang,

Fang

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Power transformers, being pivotal in electrical power systems, necessitate vigilant monitoring for operational sustainability and reliability. The traditional approach to such monitoring, Dissolved Gas Analysis (DGA), has been grounded in gas volume fraction ratio analysis-a technique that, while effective, is constrained by static thresholds and simplistic assumptions that often do not encapsulate the intricacies of fault progression. In light of these limitations, the exploration of Artificial Intelligence (AI) methodologies presents a compelling alternative, offering a dynamic and datadriven avenue for fault detection and diagnosis. This paper proposes a transformer fault diagnosis method based on the Gramian Angular Field (GAF) transformation and Graph Convolutional Networks (GCN). Firstly, the one-dimensional DGA fault samples are converted into three-dimensional DGA feature image samples using the Gramian Angular Field transformation method. Then, a dataset expansion is performed on the image samples using a GAN-based data augmentation method to meet the data volume input requirements of the deep learning model. Subsequently, a graph convolutional network based on GCN layers and a Self-Attention Graph Pooling (SAGPool) mechanism is constructed. This network aggregates neighbor nodes according to the node features and topological structure of the threedimensional DGA graph signal to adaptively mine fault features and diagnose fault types. The experimental results show that the method proposed in this paper can serve as an effective transformer fault diagnosis method. It is more accurate compared to the traditional gas ratio method for diagnosis, and its advantages are more significant compared to other traditional fault diagnosis deep learning methods.

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Section: Image Data Augmentation Methods Based On Ganmentioning

confidence: 99%

Transformer fault diagnosis method based on Gramian angular field and graph convolution network

zhang,

Yang,

Fang

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…Figure 2 depicts a flow chart illustrating the steps used to determine the fault type of the transformer. (23) First, concentrations of key gases are obtained by extracting the gases and separating them via the GC machine. After that, it is checked whether one of the gas concentrations of H 2 , CH 4 , C 2 H 2 , and C 2 H 4 exceeds the limit L1 values shown in Table 3.…”

Section: Doernenburg Methodsmentioning

confidence: 99%

Automated Dissolved Gas Analysis Report Generator for Monitoring Condition of High-voltage Transformers

Chanchotisatien¹,

Phumpho²

2021

Sensors and Materials

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Dissolved gas analysis (DGA) is a commonly used method that allows pending or occurring faults within transformers to be determined. DGA has been widely used for many years. There are various DGA techniques, including the evaluation of individual and total dissolved combustible gas (TDCG) concentrations, the Doernenburg method, the Rogers method, and Duval's triangle. To facilitate the process of monitoring the condition of transformers and reduce potential human error, we discuss how to create a user-friendly system to monitor and evaluate the condition of high-voltage transformers. The system receives key gas values, which are extracted from oil samples inside transformers obtained by gas chromatography (GC), and is capable of automatically generating downloadable reports of specified transformers and gives insight on any faults found. It also visualizes the changes in extracted key gas values of transformers over time. To develop this system, the front end of the application was made with HTML and CSS, and the back end was made with JavaScript with MySQL as a database combined with Microsoft SharePoint. The automatic downloadable report generator was made with Power Automate. This system assists the monitoring of transformer conditions.

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“…Conventional analysis techniques of dissolved gases can be performed by analysing different gas concentration ratios (Doernenburg ratios, Rogers ratios, and Duval's triangle method) [2,3]. Artificial intelligence (AI)-based methods have been introduced to improve the diagnosis accuracy and remove the inherent uncertainty in DGA.…”

Section: Introduction *mentioning

confidence: 99%

A Comparative Study of Machine Learning Classifiers in Oil-Immersed Power Transformer Fault Diagnosis

2023

JST: SSAD

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The most common fault diagnosis method for oil-immersed power transformers is dissolved gas analysis (DGA). Doernenburg ratios, Rogers ratios, IEC (International Electrotechnical Commission) ratios, and Duval's triangle are conventional DGA techniques for insulating oil in power transformers. In this study, Scikit-learn known as a popular open-source free machine learning tool for Python programming language has been used to develop different machine learning (ML) classifiers to effectively detect defects in oil-immersed power transformers. These classifiers are Decision Trees, Support Vector Machines, Gaussian Naive Bayes, k-Nearest Neighbours, Random Forests, and Multi-Layer Perceptron. The input vector of each classifier has been formed by Doernenburg ratios, Rogers ratios, IEC ratios, and CSUS (California State University Sacramento) method. After these classifiers are completely trained, unseen DGA data sets are then used to evaluate their performances. Based on a statistical analysis, the study can indicate the most effective type of the input vector and ML classifier for precisely detecting faults in power transformers.

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Comparative study between dorneneburg and rogers methods for transformer fault diagnosis based on dissolved gas analysis using Matlab Simulink Tools

Cited by 9 publications

References 13 publications

Transformer fault diagnosis method based on Gramian angular field and graph convolution network

Transformer fault diagnosis method based on Gramian angular field and graph convolution network

Automated Dissolved Gas Analysis Report Generator for Monitoring Condition of High-voltage Transformers

A Comparative Study of Machine Learning Classifiers in Oil-Immersed Power Transformer Fault Diagnosis

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