Parameter and VSI Nonlinearity Hybrid Estimation for PMSM Drives Based on Recursive Least Square
Chuanqiang Lian, Fei Xiao, Jilong Liu, Shan Gao
Abstract
Precise electrical parameters play important roles in the high-performance control of permanent magnet synchronous machines (PMSMs). This article proposes a novel parameter and voltage source inverter (VSI) nonlinearity hybrid estimation method to accurately estimate stator resistance, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$dq$ </tex-math></inline-formula> -axis inductances, permanent magnet flux linkage, and VSI nonlinearity, in which the effects of magnetic saturation, cross saturation, and temperature are all considered. The proposed hybrid estimation method consists of two parts: offline estimation and online estimation. In the offline estimation, the four electrical parameters are successively identified by setting different operating conditions, and the identification results are stored in nonvolatile memory in a tabular form. In the online estimation, the VSI nonlinearity and the compensation terms of stator resistance and permanent magnet flux linkage related to factors, such as temperature and frequency, are simultaneously identified by using the recursive least square (RLS) algorithm. Experimental results on a 300-kW PMSM drive system demonstrate that compared to the results achieved with the existing method, the proposed scheme achieves higher estimation accuracy. Consequently, the control performance of the system, such as the output current quality, is efficiently improved.