Online Estimation of Double-Star IPM Machine Parameters Using RLS Algorithm

Abstract

Accurate and updated knowledge of the electric machine parameters provides benefits in many applications, such as model-based control methods. This paper presents an online parameter estimation method for double-star interior permanent-magnet (IPM) machines supplied with voltage-source inverters (VSI). The estimation method is based on a decoupled D-Q model of the machine and a recursive least-squares (RLS) algorithm. Initial machine parameters are estimated at a standstill. Then, in the rotating operating state, the inductances and the flux linkage produced by the PMs are updated. The estimation in the operating state is performed with a flux-linkage-based approach. The experimental results are compared with the corresponding values obtained from finite-element analyses. The results show an acceptable agreement and demonstrate the feasibility of the estimation scheme to estimate the parameters of double-star PM machines. The parameters are validated by comparing experimental and simulated open-loop responses and steady-state torques.