基于自适应神经网络观测的无电压传感器PWM整流器功率预测控制

Voltage-sensorless Model Predictive Power Control of PWM Rectifier Based on Adaptive Neural Network Observation

模型预测控制在脉宽调制(pulse width modulation,PWM)整流器上的应用既降低了直接功率控制中的脉振又提高了动态响应速度,但是传统的模型预测功率控制(model predictive power control,MPDPC)中对未来时刻状态量的预测仅依靠模型,对模型参数变化较为敏感,功率预测精度受电压传感器的测量精度和网侧谐波变化的影响明显。为实现整流侧参数的实时辨识和提高整体的预测精度,以实现对功率的精准控制,文中在模型预测功率控制(model predictive power control,MPDPC)的基础上引入自适应神经网络电压观测器,提出基于自适应神经网络观测的无电压传感器PWM整流器功率预测控制(adaptive neural model predictive power control,ANMPDPC)策略。通过构建包含自适应神经网络辨识器和自适应神经网络滤波器的自适应电压观测器,实现网侧电压估计的同时滤除电压高次谐波对其的影响,并将电压观测器与功率二步预测相结合,进一步降低功率脉振,提高系统的响应速度和控制精度。仿真和实验结果表明,所提出的改进策略既实现了无电压传感器下的模型预测控制,又有效抑制了网侧谐波的高频干扰及参数变化对预测精度的影响。

Model predictive control is applied to the PWM rectifier to reduce the pulse vibration in the direct power control and improve the dynamic response speed. However, the prediction of the state quantity of the future time in the traditional model predictive power control(MPDPC) depends only on the model. The parameter variation of the model is very sensitive. The measurement of voltage sensor and the influence of grid side harmonics limit the power prediction. In order to realize the real-time identification of the parameters of the rectification side and improve the overall prediction accuracy, achieve precise control of power. Based on the introduction of adaptive neural network voltage observer, a voltage-sensorless model predictive power control of PWM rectifier based on adaptive neural network observation(ANMPDPC) was proposed. By constructing an adaptive voltage observer including an adaptive neural network identifier and an adaptive neural network filter, to realize the influence of voltage higher harmonics on the grid side voltage estimation, and the voltage observer and power two. The combination of step prediction further reduces the power pulse vibration and improves the response speed and control accuracy of the system. The simulation and experimental results show that the proposed improved strategy not only achieves the model predictive control under the no-voltage sensor, but also effectively suppresses the influence of the high-frequency interference of the grid side harmonics and the parameter variation on the prediction accuracy.