Enhanced prediction of transformers vibrations under complex operating conditions

Abstract

Vibrations occurring in transformer are a physical phenomenon that can be used for condition monitoring, since when the amount of vibrations changes significantly, a faulty condition is in progress (or is incipient). Consequently, vibration prediction becomes crucial for condition monitoring of transformers; however accurately predicting them is challenging, especially in complex scenarios like unbalanced loads and current harmonics. To address this challenge, two methodologies are introduced: one employs a Random Forest (RF) algorithm while the second one is a physical based model. Both methodologies use current information as inputs for vibration prediction, with temperature information serving as additional inputs in the machine learning-based model. Experimental tests, conducted on a distribution transformer during real operations and exposed to unbalanced loads and harmonic currents, demonstrate that both methods are capable of predicting the fundamental component of the vibrations, together with higher harmonics with different degrees of accuracy. The proposed methodologies seem promising as techniques for early diagnosis of faults in transformers or used as an aid to implement possible preventive maintenance techniques.