An Offline Parameter Self-Learning Method Considering Inverter Nonlinearity With Zero-Axis Voltage
An Offline Parameter Self-Learning Method Considering Inverter Nonlinearity With Zero-Axis Voltage
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DOI：
10.1109/tpel.2021.3089544
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发表时间：
2021-06
影响因子：
6.7
通讯作者：
Qiwei Wang;Nannan Zhao;Gaolin Wang;Shouhua Zhao;Zhixue Chen;Guoqiang Zhang;Dianguo Xu
中科院分区：
文献类型：
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作者：
Qiwei Wang;Nannan Zhao;Gaolin Wang;Shouhua Zhao;Zhixue Chen;Guoqiang Zhang;Dianguo Xu
在电压型逆变器应用中，逆变器的非线性会在很多方面影响参数辨识过程。因此，本文提出了一种离线识别方法的电阻和dq轴电感表面考虑逆变器的非线性特性。提出了一种用于dq轴电感辨识的变幅方波注入法。VASI方法通过一种新颖的数据采样策略实现了电感参数的辨识。同时，该方法还可以通过多项式拟合算法，仅用少量识别数据点建立电感曲面，与现有方法相比，大大缩短了识别时间。电阻辨识采用斜坡信号注入法，分析了IGBT压降对电阻辨识的影响。为了提高辨识精度，逆变器的非线性补偿的自学习方法，考虑到零轴电压在不同的转子位置。同时，对abc相零电流区的采样误差进行了研究。为了验证该方法的有效性和通用性，在两台不同的试验机上进行了试验，并通过有限元分析进行了验证。
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.