A long short-term memory based prediction model for transformer fault diagnosis using dissolved gas analysis with digital twin technology
TL;DR: In this paper , a long short-term memory (LSTM) based prediction model is developed to train the digital twin for identifying the essential fault in the transformer via DGA.
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Abstract: <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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References
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603
IEEE and IEC Codes to Interpret Incipient Faults in Transformers, Using Gas in Oil Analysis
TL;DR: In this article, the Buchholz relay was used to detect incipient faults in oil immersed transformers by examination of the gases dissolved in the oil developed from the original Buchholtz relay application.
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A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning
TL;DR: A two-phase digital-twin-assisted fault diagnosis method using deep transfer learning (DFDD), which realizes fault diagnosis both in the development and maintenance phases and ensures the accuracy of the diagnosis as well as avoids wasting time and knowledge.
Dissolved gas analysis evaluation in electric power transformers using conventional methods a review
Jawad Faiz,Milad Soleimani +1 more
TL;DR: In this article, the authors evaluated dissolved gas analysis (DGA) interpretation in detecting different faults and the techniques considered as conventional methods of DGA are investigated based on DGA data obtained from oil samples of real transformers.
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Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine
TL;DR: The fault diagnosis results of IEC TC 10 database show that the proposed ODGR with SVM may be used as an alternative tool for transformer fault diagnosis and the robustness and generalization ability of ODGR is confirmed.
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