基于抗频率偏移电感辨识的并网逆变器模型预测控制

电感参数对实现并网逆变器高精度模型预测控制至关重要，而传统电感辨识方法易受到电网频率偏移的影响，且在有功功率为零时无法使用。为提高模型预测控制中电感参数的频率鲁棒性，提出了一种基于模型参考自适应的电感在线辨识方法。首先，设计了一种二阶滑模观测器观测电网电压。其次，在不补偿低通滤波器造成观测电压幅值和相位偏差的情况下，令实际电网电压也产生相同的幅值和相位偏差，而后利用二者之间的误差与电感误差的关系建立电感辨识模型，从而克服了电网频率偏移对电感辨识结果的影响，且在有功功率为零时也可实现电感辨识。最后，将辨识出的电感参数代入模型预测控制算法，即可实现对逆变器更准确地控制。实验研究验证了所提电感辨识方法的有效性和准确性。

Model predictive control for a grid-connected inverter based on inductance identificationresistant to frequency deviation

The inductance parameter is crucial in realizing high precision model predictive control for a grid-connected inverter. The traditional inductance identification method is easily affected by grid frequency deviation and cannot be used when the active power is zero. To improve the frequency robustness of the inductance parameter in model predictive control, an online identification method based on MRAS is proposed. First, a second-order sliding mode observer is designed to observe the grid voltage. Second, without compensating for the observed voltage amplitude and phase deviation caused by the low-pass filter, the actual grid voltage also generates the same amplitude and phase deviation. Then, using the relationship between the voltage and inductance errors, an inductance identification model is established to overcome the impact of grid frequency deviation on the results, and the identification is also available when the active power is zero. Finally, by incorporating the identified parameter into the model predictive control algorithm, the control of the inverter can be more accurate. The effectiveness and accuracy of the proposed method is verified by experiment.