Computer Modeling of the Eddy Current Losses of Metal Fasteners in Rotor Slots of a Large Nuclear Steam Turbine Generator Based on Finite-Element Method and Deep Gaussian Process Regression

Abstract

Eddy current analysis is a key issue for large turbine generators. The finite-element method (FEM) is a computational tool for obtaining the electromagnetic characteristics of electrical machines. In this article, we propose a computer model of the eddy current losses of metal fasteners in the rotor slots of a large turbine generator. The electromagnetic properties of the rotor fasteners and the outer diameter of the rotor are taken as the input, and the eddy current loss of the rotor fasteners is taken as the output. A prediction model is constructed using the FEM and deep learning. The analysis results show that compared with the independent finite-element analysis, this method reduces the design cycle time and improves the design efficiency for a large-capacity turbine generator. Compared with other machine learning models, the error is smaller and the accuracy is higher. This method provides a new way to accurately predict the eddy current loss of a generator under complex nonlinear conditions.