异步电机无速度传感器矢量控制系统研究
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摘要
异步电机无速度传感器矢量控制技术提高了交流传动系统的可靠性,降低了系统的实现成本。准确辨识电机转速是实现无速度传感器矢量控制的关键。
本文对无速度传感器矢量控制系统进行了研究,建立了异步电动机无速度传感器电压解耦矢量控制系统和基于模型参考自适应(MRAS)的无速度传感器矢量控制系统。基于MRAS的无速度传感器矢量控制系统利用电动机定子电压方程和电流方程得到电动机转速的模型参考自适应辨识算法,在此基础上建立了一个改进的变参数MRAS速度辨识数学模型,并利用Matlab软件对基于该速度辨识模型的无速度传感器异步电动机矢量控制系统在不同的情况下进行了详细的仿真研究。仿真结果验证了该改进的变参数MRAS速度辨识模型具有令人满意的辨识精度和动态性能。
基于MRAS的转速估算理论从本质上来说属于基于电机理想模型的转速估算方案,该方法依赖于电机参数,而电机参数在电机运动过程中变化很大,因而给出了对电机的一些定、转子参数进行实时辨识方法,以保持系统的动、静态性能。
在传统型模型参考自适应系统基础上,将系统中原有的自适应调节机构用一个具有在线学习能力的人工神经网络取代,提出一种基于神经网络的异步电机转速估计方法,并给出了速度估计器的神经网络结构和学习算法。最后对基于神经网络转速估计的异步电机矢量控制系统进行了仿真,结果表明该系统具有良好的性能。
简单介绍了基于DSP的异步电机无速度传感器矢量控制系统的硬件结构以及软件系统的设计。
The speed sensorless vector control of induction motor technology enhances the reliability of AC driving system, and reduces the realization cost. The key problem is that how we can get the speed of motor accurately.
This thesis made a study on the speed-sensorless vector control system. And built up a induction speed-sensorless vector control system on based voltage decoupling and a speed-sensorless vector control system on based model reference adaptive system (MRAS). According to the reduced order model of induction motor, a model reference adaptive speed estimation algorithm was obtained from the equations of induction motor stator voltages and currents. An improved model MRAS speed estimation model was built with variable parameters. The detailed simulation researches were tested on the speed-sensorless vector control system of induction motor using the software MatLab/Simulink. The simulation results show that this improved MRAS speed estimation model has satisfied estimation precision and dynamic performance. Some simulation models can be used in other VVVF control strategy system also.
The speed estimation theory based on MRAS, in essence, is based on the ideal model of the motor speed estimation programme, which are dependent on the electrical parameters, the electrical parameters in the process of motor sports has changed greatly and thus give some of the motor stator and rotor parameters real-time identification method, in order to maintain the dynamic and static performance.
Based on the traditional adaptive system, the neural network replaces the original organization in the paper. A kind of new method of speed estimation based on neural network, and the structure and learning algorithm of neural network are proposed in the paper. The simulation result shows that the vector control system of asynchronous motor based on speed estimation of fuzzy neural network has a good performance.
This thesis also makes an introduction of the speed-sensorless vector control system based on DSP, the hardware circuit and the software process.
本文对无速度传感器矢量控制系统进行了研究,建立了异步电动机无速度传感器电压解耦矢量控制系统和基于模型参考自适应(MRAS)的无速度传感器矢量控制系统。基于MRAS的无速度传感器矢量控制系统利用电动机定子电压方程和电流方程得到电动机转速的模型参考自适应辨识算法,在此基础上建立了一个改进的变参数MRAS速度辨识数学模型,并利用Matlab软件对基于该速度辨识模型的无速度传感器异步电动机矢量控制系统在不同的情况下进行了详细的仿真研究。仿真结果验证了该改进的变参数MRAS速度辨识模型具有令人满意的辨识精度和动态性能。
基于MRAS的转速估算理论从本质上来说属于基于电机理想模型的转速估算方案,该方法依赖于电机参数,而电机参数在电机运动过程中变化很大,因而给出了对电机的一些定、转子参数进行实时辨识方法,以保持系统的动、静态性能。
在传统型模型参考自适应系统基础上,将系统中原有的自适应调节机构用一个具有在线学习能力的人工神经网络取代,提出一种基于神经网络的异步电机转速估计方法,并给出了速度估计器的神经网络结构和学习算法。最后对基于神经网络转速估计的异步电机矢量控制系统进行了仿真,结果表明该系统具有良好的性能。
简单介绍了基于DSP的异步电机无速度传感器矢量控制系统的硬件结构以及软件系统的设计。
The speed sensorless vector control of induction motor technology enhances the reliability of AC driving system, and reduces the realization cost. The key problem is that how we can get the speed of motor accurately.
This thesis made a study on the speed-sensorless vector control system. And built up a induction speed-sensorless vector control system on based voltage decoupling and a speed-sensorless vector control system on based model reference adaptive system (MRAS). According to the reduced order model of induction motor, a model reference adaptive speed estimation algorithm was obtained from the equations of induction motor stator voltages and currents. An improved model MRAS speed estimation model was built with variable parameters. The detailed simulation researches were tested on the speed-sensorless vector control system of induction motor using the software MatLab/Simulink. The simulation results show that this improved MRAS speed estimation model has satisfied estimation precision and dynamic performance. Some simulation models can be used in other VVVF control strategy system also.
The speed estimation theory based on MRAS, in essence, is based on the ideal model of the motor speed estimation programme, which are dependent on the electrical parameters, the electrical parameters in the process of motor sports has changed greatly and thus give some of the motor stator and rotor parameters real-time identification method, in order to maintain the dynamic and static performance.
Based on the traditional adaptive system, the neural network replaces the original organization in the paper. A kind of new method of speed estimation based on neural network, and the structure and learning algorithm of neural network are proposed in the paper. The simulation result shows that the vector control system of asynchronous motor based on speed estimation of fuzzy neural network has a good performance.
This thesis also makes an introduction of the speed-sensorless vector control system based on DSP, the hardware circuit and the software process.
引文
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[6]王兆安,黄俊.电力电子技术.北京:机械工业出版社,2000
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[22]王文森,李永东,王光辉等.基于PI自适应法的无速度传感器异步电动机矢量控制系统.电工技术学报,2002,17(1):1-6.
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[27]谢小竹,彭彬.MATLA电力系统设计与分析.北京:国防工业出版社,2004:288-343页
[28]王焕刚,徐文立.感应电机无速度传感器控制的自适应转速估计.电气传动.2002,32(1):6-9页
[29]邱阿瑞,孙健.用人工神经网络实现对感应电机转速的估计.清华大学学报,1999,39(9):29-32
[30]邱阿瑞,孙健.利用人工神经网络进行感应电动机解耦控制.清华大学学报,1999,39(7):13-15
[31]Chih-Min Lin.Neural Network-Based Adaptive Control for Induction Servomotor Drive System.IEEE Transaction On Industry Electronics,2002,49(1):115-123
[32]Yoon-Ho Kim.Neural Network-Based Speed Sensorless induction Motor drive with Kalman Filter Approach.IEEE Transaction On Industry Apllications,1998,34(3):997-1001
[33]Sensorless Field Oriented Speed Control of Three Phase AC Induction Motor using TMS320F240.Texas Instruments Europe,1998
[34]Digital Signal Processing Solution for AC Induction Motor.Texas Instruments Inc.Literature Number:BPRA043,1996
[35]Colin Schauder,"Adaptive Speed Identification for Vector Control of Induction Motors without Rotational Transducers",Conf.Rec.IEEE-IAS annual Meeting,pp.493-499,1989
[36]Hirokazu Tajima,and Yoichi Hori,"Speed Sensorless Field-Orientation Control of the Induction Machine",IEEE Trans.Ind.Appl.vol.29,NO.1,pp.175-180,1993
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