Case studies on transformer fault diagnosis using dissolved gas analysis

Shanker, T. Bhavani; Nagamani, H.N.; Antony, Deepthi; Punekar, Gururaj S.

doi:10.1109/appeec.2017.8309010

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…These gases are considered to provide useful information for the maintenance of the transformer. (4) Gases in the transformer are quantified via the gas chromatography (GC), which analyzes the chemical components of a sample mixture to determine how much of each component is present. Dissolved gas analysis (DGA) is the study of key dissolved gases in transformer oil and is helpful for determining the condition and identifying underlying faults in power transformers.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Some gases will be produced, such as low molecular hydrocarbons like CH 4 , C 2 H 6 , C 2 H 4 and C 2 H 2 , as well as gases of CO, CO 2 and H 2 . When partial discharge or overheating occurs in the transformer, the gas will be produced in large quantities, so the analysis of the gas content in the oil to diagnose the transformer fault is a trend in recent years [1]. In addition, the dissolved gases analysis (DGA) in oil will not be affected by external electric fields and magnetic fields, and it can detect the latent faults existing inside the transformer and can become an effective method for the fault diagnosis of oil-immersed transformers [2].…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

Li¹,

Ma²

2019

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A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarm optimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.

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