Aging Assessment of Power Transformers with Data Science

Samuel Lessinger (), Alzenira da Rosa Abaide, Rodrigo Marques de Figueiredo, Lúcio Renê Prade and Paulo Ricardo da Silva Pereira
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Samuel Lessinger: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
Alzenira da Rosa Abaide: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
Rodrigo Marques de Figueiredo: Polytechnic School, University of Vale do Rio dos Sinos, São Leopoldo 93022-750, Rio Grande do Sul, Brazil
Lúcio Renê Prade: Polytechnic School, University of Vale do Rio dos Sinos, São Leopoldo 93022-750, Rio Grande do Sul, Brazil
Paulo Ricardo da Silva Pereira: Polytechnic School, University of Vale do Rio dos Sinos, São Leopoldo 93022-750, Rio Grande do Sul, Brazil

Energies, 2025, vol. 18, issue 15, 1-33

Abstract: Maintenance techniques are fundamental in the context of the safe operation of continuous process installations, especially in electrical energy-transmission and/or -distribution substations. The operating conditions of power transformers are fundamental for the safe functioning of the electrical power system. Predictive maintenance consists of periodically monitoring the asset in use, in order to anticipate critical situations. This article proposes a methodology based on data science, machine learning and the Internet of Things (IoT), to track operational conditions over time and evaluate transformer aging. This characteristic is achieved with the development of a synchronization method for different databases and the construction of a model for estimating ambient temperatures using k-Nearest Neighbors. In this way, a history assessment is carried out with more consistency, given the environmental conditions faced by the equipment. The work evaluated data from three power transformers in different geographic locations, demonstrating the initial applicability of the method in identifying equipment aging. Transformer TR1 showed aging of 3.24 × 10 − 3 % , followed by TR2 with 8.565 × 10 − 3 % and TR3 showing 294.17 × 10 − 6 % in the evaluated period of time.

Keywords: machine learning; predictive maintenance; power transformers; environmental conditions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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