Turkey|Electrical Engineering Department;
Control and Automation Engineering Department;
Turkey;
Istanbul;
Istanbul Technical University;
AVL Türkiye Research and Engineering;
Electrical Engineering Department;
关键词：
Resistance;
Parameter estimation;
Least squares approximations;
Fast Fourier transforms;
Estimation;
Harmonic analysis;
Motors;
Synchronous motors;
Mathematical models;
Power harmonic filters;
期刊名称：
IEEE Access
i s s n：
2169-3536
年卷期：
2024 年
12 卷
页 码：
70220-70234
页 码：
摘 要：
Permanent magnet assisted synchronous reluctance motors (PMaSynRM) are popular motors in today’s industry because their cost, performance, ease of control, field weakening (FW) and robustness are comparable to their competitors. High performance control of these machines requires a priori knowledge of motor parameters and the internal and external effects on the variation of these parameters, such as temperature, permanent magnet (PM) demagnetization, and magnetic saturation. The advantages of on-line identification can be used to reduce the development effort and the use of look-up tables for control, instead of experimental and off-line identification. Data-based identification of dynamic systems is a promising area for electric motor parameter identification. Such methods require a data set containing correctly excited system dynamics – so called persistent excitation condition over the entire identification data set. Although voltage source inverter, magneto-motive force (MMF) and flux harmonics can be used for parameter estimation, this is not sufficient in most cases and usually additional identification signals are injected into the system, causing unwanted disturbances such as torque and speed ripple or acoustic noise. This study proposes a near-zero torque ripple current injection method to provide a persistent excitation for the PMaSynRM to generate a parameter identification data set in real time. The identification data is recorded for two electrical periods and a fast Fourier transform (FFT) based denoising and thresholding is applied to the frequency domain components to retain only the relevant components for identification. Least squares-based regression is then applied to the identification data on harmonics extended q-axis difference model to obtain the d&q-axes inductances ( $L_{d}$ & Lq) and the stator winding resistance ( $R_{s}$ ). The PM flux linkage ( $lambda _{m}$ ) is then derived for this operating point using d-axis fundamental voltage equation. The proposed method achieves good estimation accuracy without degrading the speed control with average errors of 3.66%, 4.82%, 1.3% and 3.25% for $L_{d}$ , $L_{q}$ , $R_{s}$ and $lambda _{m}$ respectively under an equally spaced torque-speed measurement grid. This results in a system capable of estimating all the parameters on-line with very high accuracy compared to the literature.
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