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An Offline Parameter Self-Learning Method Considering Inverter Nonlinearity With Zero-Axis Voltage.
- Source :
-
IEEE Transactions on Power Electronics . Dec2021, Vol. 36 Issue 12, p14098-14109. 12p. - Publication Year :
- 2021
-
Abstract
- In the voltage source inverter applications, inverter nonlinearities would affect the parameter identification process in many ways. Hence, this article proposes an offline identification method for resistance and dq-axis inductance surface by considering the inverter nonlinearity characteristics. A variable amplitude square-wave injection (VASI) scheme is proposed for the dq-axis inductance identification. The VASI method achieves the inductance identification with a novel data sampling strategy. Meanwhile, it can also establish the inductance surfaces by only a few identified data points with a polynomial fitting algorithm, which greatly reduces the identification time compared with the existing methods. The resistance identification is realized by a slope signal injection method, in which the effect of IGBT voltage drop is analyzed. In order to improve the identification accuracy, the inverter nonlinearities are compensated by a self-learning method considering the zero-axis voltage at different rotor positions. At the same time, the sampling error in zero current zones of abc-phases is researched. In order to verify the effectiveness and generality, the proposed method is carried out on two different test machines and confirmed by finite element analysis. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08858993
- Volume :
- 36
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Power Electronics
- Publication Type :
- Academic Journal
- Accession number :
- 153188068
- Full Text :
- https://doi.org/10.1109/TPEL.2021.3089544