Power Transformer Vibration Analysis Model Based on Ensemble Learning Algorithm

Abstract

The inflow of DC magnetic flux into the core of power transformers under DC bias conditions leads to a significant increase in operational losses, noise levels, and vibration amplitudes, posing substantial threats to the stability and reliability of the power system. To comprehensively examine the effects of DC bias on the vibration characteristics of power transformers, a fully coupled 3D finite element model of the transformer's electromagnetic-structural physical fields was developed. This model was validated through experimental comparison, confirming its accuracy in representing physical behaviors. In the second phase, a parametric finite element model, combined with the Sobol method, was utilized to design a set of samples for sensitivity analysis, enabling a quantitative study of the interactions among key characteristic parameters. Finally, a prediction model based on ensemble learning was proposed, using the Stacking method to integrate decision tree regression, support vector machine, and extreme learning machine algorithms. Three mainstream heuristic algorithms were applied for hyperparameter optimization, with the genetic algorithm (GA) proving to best meet the requirements, limiting the maximum prediction error to 1.2 dB. The model was validated on two in-service transformers operating under extreme conditions - DC bias and significant load fluctuations, the maximum error is less than 3 dB, demonstrating certain generalizability.