Modified FEA and ExtraTree algorithm for transformer Green's function modelling
This work addresses the computational bottleneck of FEA for transformer Green's function estimation, offering a faster alternative for engineers in power transformer design.
The authors propose a method combining modified finite element analysis (FEA) with the ExtraTree algorithm to efficiently estimate the Green's function of a transformer for vibration prediction, reducing FEA calculation time by selecting a subset of frequency response functions via a genetic algorithm and predicting the remainder with ExtraTree.
The Green's function of a transformer is essential for prediction of its vibration. As the Green's function cannot be measured directly and completely, the finite element analysis (FEA) is typically used for its estimation. However, because of the complexity of the transformer structure, the calculations involved in FEA are time consuming. Therefore, in this paper, a method based on FEA modified by an extremely random tree algorithm call ExtraTree is proposed to efficiently estimate the Green's function of a transformer. A subset of the frequency response functions from FEA will be selected by a genetic algorithm that can well present the structural variation. The FEA calculation time can be reduced by simply calculating the frequency response functions on this subset and predicting remainder using the trained ExtraTree model. The errors introduced in this method can be estimated from the corresponding frequency and the genetic algorithm error.