Indirect Measurement and Extreme Learning Machine Based Modelling for Flux Linkage of Doubly Salient Electromagnetic Machine

Doubly salient electromagnetic machines (DSEMs), which are characterised with fault tolerance, low cost, high reliability, and de-excitation ability, are gaining more and more attention in safety-critical and hash environment applications, such as the aircraft generation systems. Nevertheless, the non-linear and strong coupled characteristics of the flux linkage is the obstruct crux in DSEM modelling. The DSEM model is the critical part of the system model, which is the foundation of theoretical analysis, control strategy developing, and stability analysis. This study is aimed to demonstrate the feasibility of indirect flux linkage measurement method, as well as the effectiveness of the extreme learning machine (ELM)-based flux linkage modelling method. The basic principles of the indirect measurement are analysed and the measurement processes excluding rotor-clamping devices are proposed. The ELM is employed to high-precision flux linkage modelling with high efficiency. A three-phase 12/8-pole DSEM is tested to confirm the validity of the proposed modelling method. Both finite element analysis and experimental results are presented, verifying the effectiveness of the indirect flux linkage measurement and the ELM-based modelling method.