Generation of synthetic datasets for transformer’s dissolved gas analysis using Monte-Carlo Simulation

Eaby Kollonoor Babu, IMRAN Bashir, Gobind Pillai, Kiran Chandrakumar Jyothi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

The fault diagnosis in power transformers is carried out using Dissolved Gas Analysis (DGA). Although DGA does provide key information for fault detection, the method is inherently complex. Several methods have been developed for DGA, but still possess challenges in accurately detecting the fault. A method has been developed to generate synthetic data using Monte-Carlo simulation. The generated synthetic data is feed into DGA excel tool to investigate the accuracy of fault detection. The synthetic data can be used to further enhance the DGA tool, improve its accuracy and investigate the inclusive faults. A model has been proposed for the integration of synthetic data generator with DGA tool for machine learning and to obtain an automated and improved DGA tool for fault diagnoses in power transformers.
Original languageEnglish
Title of host publication2021 56th International Universities Power Engineering Conference
Subtitle of host publicationPowering Net Zero Emissions, UPEC 2021 - Proceedings
PublisherIEEE
ISBN (Electronic)9781665443890
DOIs
Publication statusPublished - 30 Sep 2021
Event56th International Universities Power Engineering Conference - Middlesbrough, United Kingdom
Duration: 31 Aug 20213 Sep 2021
https://www.ieee-pes.org/meetings-and-conferences/conference-calendar/monthly-view/166-technically-cosponsored-by-pes/883-upec-2021

Publication series

Name2021 56th International Universities Power Engineering Conference (UPEC)

Conference

Conference56th International Universities Power Engineering Conference
Abbreviated titleUPEC
Country/TerritoryUnited Kingdom
CityMiddlesbrough
Period31/08/213/09/21
Internet address

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