Abstract
The power transformer is one of the key components of any electrical grid, and, as such, modern day industrialization activities require constant usage of the asset. This increases the possibility of failures and can potentially diminish the lifespan of a power transformer. Dissolved gas analysis (DGA) is a technique developed to quantify the existence of hydrocarbon gases in the content of the power transformer oil, which in turn can indicate the presence of faults. Since this process requires different chemical analysis for each type of gas, the overall cost of the operation increases with number of gases. Thus said, a machine learning methodology was defined to meet two simultaneous objectives, identify gas subsets, and predict the remaining gases, thus restoring them. Two subsets of equal or smaller size to those used by traditional methods (Duval’s triangle, Roger’s ratio, IEC table) were identified, while showing potentially superior performance. The models restored the discarded gases, and the restored set was compared with the original set in a variety of validation tasks.
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The datasets generated during and/or analyzed during the current study are not publicly available due to data privacy and proprietarity.
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Acknowledgements
This work was supported by Fundo Europeu de Desenvolvimento Regional (FEDER) and Programa Operacional Competitividade e Internacionalização through the Project TRF4p0-TRANSFORMER4.0: DIGITAL REVOLUTION OF POWER TRANSFORMERS with reference POCI-01-0247-FEDER-045926.
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Pinto, J., Esteves, V., Tavares, S. et al. Optimal gas subset selection for dissolved gas analysis in power transformers. Prog Artif Intell 13, 65–84 (2024). https://doi.org/10.1007/s13748-024-00317-0
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DOI: https://doi.org/10.1007/s13748-024-00317-0