一种基于无功功率的感应电动机复合转速辨识方法

针对基于无功功率的模型参考自适应转速辨识法在动态过程中存在的收敛慢问题,提出了一种基于无功功率的复合转速辨识方法,即将基于无功功率的直接计算法和MRAS转速辨识法相结合的方法。实验结果证明,该方法既具有直接计算法的快速跟随能力,又有MRAS转速辨识法的强抗干扰能力,且算法简单,所用电动机参数少,容易在工程中实现。     

Grid-connected vector-controlled slip-ring induction machine drive with out speed sensor

A speed estimation method is presented in this paper for a grid-connected doubly-fed slip-ring induction machine drive. The proposed method is formulated with reactive power based model reference adaptive system (MRAS). The method does not require the estimation of stator/rotor flux. So, the integrator related problems at synchronous speed are overcome. Also, the estimation method is independent of stator and rotor resistance variation. Extensive simulation results are presented to validate the technique.     

Simulation studies on model reference adaptive controller based speed estimation technique for the vector controlled permanent magnet synchronous motor drive

This paper presents a speed estimation technique for the permanent magnet synchronous motor drive. A Model Reference Adaptive System (MRAS) has been formed using the instantaneous and steady-state reactive powers to estimate the speed. It has been shown that such unique MRAS offers several desirable features. The proposed technique is completely independent of stator resistance and is less parameter sensitive, as the estimation-algorithm is only dependent on q-axis stator inductance. Also, the method requires less computational effort as the simplified expressions are used in the MRAS. The stability of the proposed system is achieved through Popov’s Hyperstability criteria. Extensive simulation results are presented to validate the proposed technique. The system is tested at different speeds including zero speed and a very satisfactory performance has been achieved.     

A New Full‐Order Sliding Mode Observer Based Rotor Speed and Stator Resistance Estimation for Sensorless Vector Controlled Pmsm Drives

Sensorless control of a permanent magnetsynchronous motor (PMSM) at low speed remains a challenging task. In this paper, a sensorless vector control of PMSM using a new structure of a sliding mode observer (SMO) is proposed. To remove the mechanical sensors, a full‐order (FO‐SMO) is built to estimate the rotor position and speed of PMSM drives. The FO‐SMO, which replaces a sign function by a sigmoid function, can reduce the chattering phenomenon. In order to overcome time delay, we cancel the low pass filter. This sensorless speed control shows great sensitivity to stator resistance and system noise. To improve the robustness of sensorless vector control, a full‐order SMO technique has been used for stator resistance estimation. A novel stator resistance estimator is incorporated into the sensorless drive to compensate for the effects of stator resistance variation. The validity of the proposed FO‐SMO with a 1.1 kw low‐speed PMSM sensorless vector control is demonstrated by experiments. In this paper, experimental results for FO‐SMO, back‐EMF SMO and MRAS techniques were obtained with fixed point DSP‐based (TMS320F240).     

永磁同步电动机无速度传感器矢量调速系统的积分反步控制

基于永磁同步电动机(PMSM)的数学模型, 设计了由积分反步控制和滑模变结构模型参考自适应系统组成的无速度传感器矢量控制系统. 其中带有积分作用的反步控制作为矢量系统的速度和电流控制器, 实现给定速度和电流的无静差跟踪; 而滑模变结构模型参考自适应方法作为速度辨识器估计电机速度, 能够快速准确的跟踪实际速度. 通过Lyapunov定理证明了所设计的速度控制器和辨识器的稳定性. 仿真结果验证了所设计的无速度传感器矢量调速系统良好的速度跟踪性能和抗扰动性能.     

A new scheme for sensorless induction motor control drives operating in low speed region

A novel simple stator resistance estimation technique for high-performance induction motor drives is proposed. It makes use of a synchronously revolving reference frame aligned with the stator current vector, so that the resistance can be straightforwardly derived from the mathematical model of the induction motor. A sensorless direct field orientation scheme is employed to validate the proposed solution, with the drive operating in the critical area of low speeds. A combination of two observers is used: a Kalman filter observer to estimate the rotor flux, and a MRAS observer for speed estimation. The stator resistance estimator alleviates the usual performance degradation of MRAS-based drives at low speeds, caused by the thermal drift of stator resistance. Computer simulations, including realistic disturbances, show high effectiveness of the described approach.     

模型参考自适应方法在无速度传感器中的应用研究

通过多种无速度传感器研究方法的比较,对模型参考自适应方法进行了研究。该方法采用感应电压作状态变量,避免了积分引起的低频问题;同时采用在电压矢量方程两边都叉乘定子矢量电流的方法,显著降低了定子电阻参数变化（尤其是在低速时）的影响。最后给出了MATLAB仿真试验的结果。     

Hybrid control for speed sensorless induction motor drive

The dynamic response of a hybrid-controlled speed sensorless induction motor (IM) drive is introduced. First, an adaptive observation system, which comprises speed and flux observers, is derived on the basis of model reference adaptive system (MRAS) theory. The speed observation system is implemented using a digital signal processor (DSP) with a high sampling rate to make it possible to achieve good dynamics. Next, based on the principle of computed torque control, a computed torque controller using the estimated speed signal is developed. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a recurrent fuzzy neural network (RFNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Furthermore, based on Lyapunov stability a hybrid control system, which combines the computed torque controller, the RFNN uncertainty observer and a compensated controller, is proposed to control the rotor speed of the sensorless IM drive. The computed torque controller with RFNN uncertainty observer is the main tracking controller and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rules of the RFNN. Finally, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results     

High performance of brain emotional intelligent controller for DTC-SVM based sensorless induction motor drive

This paper introduces the application of the induction motor (IM) drive brain emotional intelligent controller (BEIC). Intelligent regulation, modelled on the human brain, is capable of generating impulses and is used as a controller. A Model Reference Adaptive System is developed using stator current and stator voltages, which are further developed with BEIC to approximate the rotor rpm. This paper proposes that speed estimation using BEIC for direct torque control (DTC) of IM drive. The experimental work is conducted on a hardware-in-loop mechanism using a real-time digital simulator (Op-RTDS-OP5600). The simulation and test results are discussed. The proposed method is compared to the DTC-SVM-based IM drive speed control with the existing controllers.

     

一种基于锁相环原理的参考模型自适应感应电机转速估计方法

针对超低速及零定子频率运行条件下感应电动机转速的不可观测性导致基于电机模型的传统速度估计方案无法实现速度估计,引入了高频信号注入法来获得转子磁链矢量位置角并得到转子磁链的参考模型,并以转子磁链的电流模型作为调节模型,在此基础上,提出了基于锁相环原理的参考模型自适应速度估计方案．仿真结果进一步验证了该方案的有效性．     

基于滑模模型参考自适应观测器的无速度传感器三相永磁同步电机模型预测转矩控制

针对三相永磁同步电机(PMSM)驱动系统,基于滑模变结构模型参考自适应(MRAS)技术,提出了一种新颖的无速度传感器模型预测转矩控制(MPTC)策略.采用滑模变结构模型参考自适应方法构造电机转速观测器,以改善速度估计精度并提高系统鲁棒性;利用模型预测转矩控制策略,以达到减小转矩和磁链纹波并提高系统控制性能的目的.仿真结果表明:就滑模MRAS观测器与MRAS观测器比较而言,基于前者的PMSM无速度传感器MPTC系统比基于后者的PMSM无速度传感器MPTC系统具有较强的鲁棒性和更好的动态性能;就MPTC与直接转矩控制(DTC)和磁场定向控制(FOC)比较而言,采用前者策略的无速度传感器电机驱动系统能够降低逆变器开关频率、减少相电流总谐波失真(THD),从而提高系统可靠性.     

基于模型参考自适应的速度估计方法

为解决无速度传感器感应电动机矢量控制系统的速度估计问题,以模型参考自适应的理论为基础,利用感应电压矢量进行转速估计并进行仿真,转速估计结果理想,仿真实验所得到的波形与理论分析结果是一致的。并且使相应的无速度传感器矢量控制系统具有良好的静、动态性能。证明了采用模型参考自适应系统,利用感应电压矢量进行转速估计方法的正确性和可行性。     

Open-loop neuro-fuzzy speed estimator applied to vector and scalar induction motor drives

Scalar and vector drives have been the cornerstones of control of industrial motors for decades. In both the elimination of mechanical speed sensor consists in a trend of modern drives. This work proposes the development of an adaptive neuro-fuzzy inference system (ANFIS) angular rotor speed estimator applied to vector and scalar drives. A multi-frequency training of ANFIS is proposed, initially for a V/f scheme and after that a vector drive with magnetizing flux oriented control is proposed. In the literature ANFIS has been commonly proposed as a speed controller in substitution of the classical PI controller of the speed control loop. This paper investigates the ANFIS as an open-loop speed estimator instead. The subtractive clustering technique was used as strategy for generating the membership functions for all the incoming signal inputs of ANFIS. This provided a better analysis of the training data set improving the comprehension of the estimator. Additionally the subtractive cluster technique allowed the training with experimental data corrupted by noise improving the estimator robustness. Simulations to evaluate the performance of the estimator considering the V/f and vector drive system were realized using the Matlab/Simulink^® software. Finally experimental results are presented to validate the ANFIS open loop estimator.     

Power control of a doubly fed induction machine via output feedback

A new output feedback control algorithm for a doubly fed induction machine (DFIM) is presented. The asymptotic regulation of active and reactive power is achieved by means of direct closed-loop control of active and reactive components of the stator current vector, presented in a line-voltage-oriented reference frame. To get the maximum generality of the solution, the usual assumption of negligible stator resistance is not made. A full-order DFIM model is used for the control algorithm development. The proposed control system is robust with respect to bounded machine parameter variations and errors on rotor position measurement. In the paper, it is also shown how the proposed current control algorithm can be modified in order to achieve asymptotic active current tracking and zero reactive current stabilization during steady state. An extension for the speed control objective and output EMF control during the excitation–synchronization stage are also presented. Simulation and experimental tests demonstrate high dynamic performance and robustness of the control algorithm for typical operating conditions. The proposed controller is suitable for both energy generation and electrical drive application with restricted speed variation range.     

Application of CMAC neural network to the control of induction motor drives

In this paper, a cerebellar-model-articulation-controller (CMAC) neural network (NN) based control system is developed for a speed-sensorless induction motor that is driven by a space-vector pulse-width modulation (SVPWM) inverter. By analyzing the CMAC NN structure and motor model in the stationary reference frame, the motor speed can be estimated through CMAC NN. The gradient-type learning algorithm is used to train the CMAC NN online in order to provide a real-time adaptive identification of the motor speed. The CMAC NN can be viewed as a speed estimator that produces the estimated speed to the speed control loop to accomplish the speed-sensorless vector control drive. The effectiveness of the proposed CMAC speed estimator is verified by experimental results in various conditions, and the performance of the proposed control system is compared with a new neural algorithm. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN.     

Comparison of two novel MRAS based strategies for identifying parameters in permanent magnet synchronous motors

Two model reference adaptive system (MRAS) estimators are developed for identifying the parameters of permanent magnet synchronous motors (PMSM) based on the Lyapunov stability theorem and the Popov stability criterion, respectively. The proposed estimators only need online measurement of currents, voltages, and rotor speed to effectively estimate stator resistance, inductance, and rotor flux-linkage simultaneously. The performance of the estimators is compared and verified through simulations and experiments, which show that the two estimators are simple, have good robustness against parameter variation, and are accurate in parameter tracking. However, the estimator based on the Popov stability criterion, which can overcome parameter variation in a practical system, is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator, in contrast to only one integral unit in the estimator based on the Lyapunov stability theorem. In addition, the estimator based on the Popov stability criterion does not need the expertise that is required in designing a Lyapunov function.     

An MRAS method for sensorless control of induction motor over a wide speed range

This paper addresses the problem of wide speed range sensorless control of induction motor.The proposed method is based on model reference adaptive system (MRAS),in which the current model serves as the adjustable model,and a novel hybrid model integrating the modified voltage model (MVM) and high-frequency signal injection method (HFSIM) are established to serve as the reference model.The HFSIM works together with MVM to improve the performance of the rotor speed and rotor flux position estimation at low speed,whereas at high speed,the MVM acts alone.In addition,a rotor resistance online estimation scheme is proposed to update the rotor resistance contained in the adjustable model and to ensure the estimation accuracy further.Experimental results show that the proposed MRAS method is very effective from low to high speed range,including zero speed.     

考虑铁损的电动汽车用感应电机矢量控制及其能量优化策略

电动汽车用感应电机励磁电感一般较小,高速时铁损大,采用经典矢量控制策略存在轻载低效和由忽略铁损引起的控制不精确等问题.首先根据同步旋转坐标系下考虑铁损的感应电机动态数学模型,分析了铁损对按转子磁场定向矢量控制的影响,给出了动态和稳态两种补偿方案.然后从调节磁通水平的角度,提出了一种基于损耗模型的感应电机能量优化控制策略,并讨论了铁损等效电阻变化对优化控制的影响.仿真和实验结果表明,给出的补偿控制策略克服了经典矢量控制磁场定向及转矩控制不准确的缺陷;提出的的能量优化控制策略不但节能效果明显,而且具有寻优速度快、转矩和转速波动小等优点,为高性能要求的电动汽车电驱动系统高效运行提供了有效途径.     

Sensorless direct torque control of an induction motor by a TLS-based MRAS observer with adaptive integration

This article presents a new speed and flux estimation algorithm for high-performance direct torque control (DTC) induction motor drives based on model reference adaptive systems (MRAS) observers using linear artificial neural networks (ANNs). Two completely new improvements of MRAS speed and flux observers are presented here: the first is a solution to the open-loop integration problem in the reference model, based on the voltage model of the induction machine, by means of a new adaptive neural integrator, the second is the employment of a new adaptation law in the ANN adaptive model, based on the total least-squares (TLS) technique. In particular, the adaptive neural integrator is based on two adaptive noise filters which completely cancel any DC drift present in the voltage or current signals to be integrated. This neural integrator does not need any a priori training of its two only neurons, adapting itself on-line. With regard to the ANN-based adaptive model, since the most suitable least-square technique to be used for training is the TLS technique, here the neuron is trained on-line by means of a TLS EXIN algorithm which is the only neural network able to solve a TLS problem recursively. Also the TLS EXIN algorithm does not require any a priori training, since it adapts itself recursively on-line. Moreover, to improve the dynamical performances of the speed loop of the drive, the adaptive model has been used as predictor, i.e. without any feed-back between its outputs and its inputs. The sensorless algorithm has been verified experimentally both on the classic DTC technique and on the DTC-SVM (space vector modulation), by adopting a proper test set-up. The speed observer has been tested in the most challenging operating conditions. The experimental results show that the dynamical performances of the sensorless drive are comparable or even better than those obtained with the corresponding DTC drives with encoders as for the medium to high-speed ranges. As for low-speed ranges, the presented sensorless DTC algorithm outcomes the performance presented in the literature for MRAS systems, thus permitting to have an accurate estimation equal or better than that obtainable with more complex observers. Finally, experimental results show that the MRAS speed observer is robust to load torque perturbations and permits zero-speed operation at no-load conditions.     

A rotor speed estimation algorithm in variable speed permanent magnet synchronous generator wind energy conversion system

A rotor speed estimation algorithm in a direct vector controlled permanent magnet synchronous generator wind energy conversion system is proposed. The proposed method is based on a simple equation obtained from the flux model of the machine and contains only stator flux and current. Constant gain recursive least squares estimator is used for implementing the speed estimation algorithm. Rotor position information used for coordinate transformation is computed using the estimated speed. Stator flux information required by the speed estimator is obtained using the stator voltage equation by implementing a programmable low pass filter. The estimated speed is used as the feedback signal for the speed control loop of the vector controlled machine side converter control system whose command speed is obtained from a wind speed sensorless maximum power point tracking controller, thus, we obtain a complete rotor speed and wind speed sensorless permanent magnet synchronous generator wind energy conversion system. Simulation is carried out to validate the performance of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.