Determination of the Energized State Operating Point of an AC Contactor Coil Using a Bayesian Neural Network

Abstract

The paper presents a method for determining the energized operating point of an AC contactor coil using the finite element method (FEM) combined with a Bayesian neural network (BNN) model. Under actual operating conditions, the magnetic flux density and air-gap length of the electromagnet directly influence the electromagnetic force within the contactor and, consequently, the overall device performance. However, these quantities are difficult to measure accurately in practice. The objective of this study is to propose a computational approach for estimating the magnetic flux density and air-gap length of the contactor by integrating machine learning techniques with FEM simulations. The proposed method establishes an electromagnetic model of the contactor coil, in which the magnetic flux density varies from 1.0 T to 1.5 T and the air-gap length ranges from 0.05 mm to 0.30 mm, generating the corresponding voltage drops across the coil. The simulated dataset is then used to train a BNN in an inverse inference direction, enabling prediction of the magnetic flux density and air-gap length from a known operating voltage. Based on these estimated quantities, the electromagnetic attraction force is calculated using FEM, facilitating analysis of the contactor’s operating characteristics and providing a foundation for design optimization in industrial applications.