Studies on the prediction of power transformer condition immersed in mineral insulating oil by applied mathematical fitting models - Mendanha, Vinicius Faria Costa; Marques, André Pereira; Dias, Yuri Andrade; Ribeiro, Cacilda de Jesus (Brazilian Journal of Production Engineering, 2024)
Power transformers are fundamental to the electrical system regarding the continuous supply of energy, thus requiring effective tools for predictive maintenance. Therefore, the objective of this work is the accurate prediction of performance indices for non-invasive predictive techniques applied to the evaluation of transformers, offering an innovative approach applicable to different scenarios. Additionally, the overall performance index of the equipment is used as a reference to support decision-making. In this sense, the methodology adopted includes curve fitting for three predictive techniques: dissolved gas analysis, physical-chemical tests, and degree of polymerization/2FAL-Furfuraldehydes. In the results, five types of fittings were tested (linear, quadratic, exponential, Gaussian, and sum of sines), and the analytical expressions that best modeled the data were determined. The worst-case scenario criterion was considered to calculate the time intervals for each classification. Validation was carried out with training/testing data splits, using the Root Mean Square Error (RMSE) as a performance metric. It is concluded that the second-degree polynomial fit is the best fit for modeling performance indices, proving the effectiveness of the methodology developed in this work.
Keywords: Fit; maintenance; performance index; power transformer; prediction