异步电机无速度传感器控制技术研究现状与展望

异步电机无速度传感器控制技术已经成为近年的研究热点，转速估计是异步电机无速度传感器控制技术的核心问题，本文对各种常见的异步电机转速估计方法进行了评价，指出了各方法的优、缺点及其适用场合，展望了今后研究的热点方向。预测了无速度传感器控制技术可能的发展方向。     

Sensorless Control of Induction Motors by Reduced Order Observer With MCA EXIN + Based Adaptive Speed Estimation

This paper presents a sensorless technique for high-performance induction machine drives based on neural networks. It proposes a reduced order speed observer where the speed is estimated with a new generalized least-squares technique based on the minor component analysis (MCA) EXIN + neuron. With this regard, the main original aspects of this work are the development of two original choices of the gain matrix of the observer, one of which guarantees the poles of the observer to be fixed on one point of the negative real semi-axis in spite of rotor speed, and the adoption of a completely new speed estimation law based on the MCA EXIN + neuron. The methodology has been verified experimentally on a rotor flux oriented vector controlled drive and has proven to work at very low operating speed at no-load and rated load (down to 3 rad/s corresponding to 28.6 rpm), to have good estimation accuracy both in speed transient and in steady-state and to work correctly at zero-speed, at no-load, and at medium loads. A comparison with the classic full-order adaptive observer under the same working conditions has proven that the proposed observer exhibits a better performance in terms of lowest working speed and zero-speed operation     

异步电机直接转矩弱磁控制研究

针对异步电机直接转矩控制系统的弱磁控制,提出了一种新的弱磁控制策略。该策略最基本的思想就是使磁链给定值跟随着转矩误差的变化。该算法不需要复杂的电机参数而且能够实现各个速度段的平滑过度。在整个运转过程中限制定子磁链不超过设定值,在不同的速度区段给定不同的定子磁链,易于实现。最后,文章通过仿真验证了该控制策略可实现异步电机直接转矩控制系统的弱磁升速和减速过程。     

交流电机变频调速讲座 第九讲 无速度传感器控制的异步电动机调速系统

凡是高性能的交流调速系统,无论是矢量控制系统,还是直接转矩控制系统或其他系统,都需要转速调节和转速反馈,因而需要能提供转速检测信号的转速传感器,例如提供模拟转速信号的测速发电机、提供数字转速信号的光电和磁性编码器等。然而在电动机轴上安装转速传感器需要保证传感器轴与电机轴的同心度,同心度不好将影响测速的精度;编码器是精密度高的设备,在温差较大、     

基于全阶观测器的异步电动机矢量控制系统

磁链观测一直是异步电机无速度矢量控制的难点,本文以异步电动机本身为参考模型,设计了全阶观测器的可调模型来估算异步电机的磁链和速度。利用Matlab软件构造了按转子磁场定向的矢量控制系统的仿真模型,采用全阶观测器的方法在逆变器仿真平台和实验平台上实现了异步机的无速度矢量控制。通过仿真和试验验证了模型的正确性,结果表明所建立的调速系统具有良好的动态性能。     

MRAS Observers for Sensorless Control of Doubly-Fed Induction Generators

This paper addresses the analysis and performance of several model reference adaptive system (MRAS) observers for sensorless vector control of doubly-fed induction machines. Small signal models allow the formal analysis of the observers for a given dynamic. The performance of each MRAS observer is analyzed, considering grid-connected and stand-alone operation. The MRAS observers are implemented in a 3.5 kW experimental prototype composed of a doubly-fed induction generator and a wind turbine emulator. Experimental results validate the predictions of the small signal models and demonstrate the performance of the sensorless methods during both steady state and variable speed wind energy generation.     

Predictive Torque Control of Induction Machines Using Immediate Flux Control

This paper proposes a new concept for the control of voltage-source inverter (VSI)-fed induction machines. The method uses a predictive algorithm and can be split into two parts. The purpose of the first part, i.e., predictive torque control (PTC), is to predict the stator reference flux vector corresponding to the reference torque at the end of the sampling interval. The second part of the method provides accurate tracing of the stator reference flux by selecting either an active or a zero voltage vector. This approach is called immediate flux control (IFC), where two possible variants are suggested. In the first variant, a simple and fast algorithm obtains minimal stator flux error by impressing either an active or a zero voltage vector throughout the entire sampling interval. Consequently, the switching frequency becomes very low, but current and torque ripple are considerable. The second IFC variant generates the stator flux more accurately by applying an active voltage vector only throughout a calculated time slot within a sampling interval, whereas, during the remaining time of the sampling interval, a zero voltage vector is impressed. As a result, higher switching frequency arises, but it is still lower than that with space vector modulation. Both IFC variants, together with PTC, require minimal processing time and were efficiently implemented in a digital signal processor, which controlled a 3-kW induction machine drive. The obtained experimental results confirm the validity of the proposed approach.     

A Remote and Sensorless Stator Winding Resistance Estimation Method for Thermal Protection of Soft-Starter-Connected Induction Machines

This paper proposes a remote and sensorless stator winding resistance estimation method for thermal protection of soft-starter-connected induction motors. By changing the gate drive signals of the thyristors in the soft starter, a small adjustable dc bias can be intermittently injected to the motor for the estimation of the stator winding resistance. Based on online and continuous monitoring of the stator winding resistance, the stator winding temperature can be monitored using only motor voltage and current. In addition, the torque pulsation caused by the injected dc bias is analyzed. It can also be controlled under an acceptable level by adjusting the level of the injected dc signal. The influence of cable resistance is also studied, and a compensation method is proposed. The proposed method has been verified by experimental results from two induction motors. The proposed stator resistance estimation method can provide remote, sensorless, and accurate thermal protection for soft-starter-connected induction motors.     

无速度传感器感应电机控制系统的现状与展望

新技术的发展给电机控制行业带来新的发展机遇。从电机控制器、无速度传感器技术方面,介绍了国内外无速度传感器感应电机控制系统中这两个领域内的研究现状及发展趋势。回顾了DSP处理器的发展历程,介绍了DSP的发展现状,从设计和应用角度分析了DSP的特点,介绍了DSP的发展前景;分析了无速度传感器技术的研究现状,指出电机低速下速度估计性能差及低频范围稳定运行问题是目前无速度传感器技术的研究热点,并提出了相应的解决方法。     

Experimental Evaluation of Braided EKF for Sensorless Control of Induction Motors

Temperature- and frequency-dependent variations of the rotor (R'_r) and stator (R_s) resistances pose a challenge in the accurate estimation of flux and velocity in the sensorless control of induction motors (IMs) over a wide speed range. Solutions have been sought to the problem by signal injection and/or by the use of different algorithms for the different parameters and states of the same motor. In this paper, a novel Extended-Kalman-Filter (EKF)-based estimation technique is developed for the solution of the problem based on the consecutive operation of two EKF algorithms at every time step. The proposed ldquobraidedrdquo EKF technique is experimentally tested under challenging parameter and load variations in a wide speed range, including low speed. The results demonstrate a significantly increased accuracy in the estimation of R_s and R'_r, as well as load torque, flux, and velocity in transient and steady state, when compared with single EKFs or other approaches taken to estimate these parameters and states in the sensorless control of IMs. The improved results also motivate the utilization of the new estimation approach in combination with a variety of control methods which depend on accurate knowledge of a high number of parameters and states.     

Sensorless Control of Induction Motor Drives Equipped With Inverter Output Filter

This paper deals with the speed sensorless vector control of an induction motor in a special case where the output voltage of the pulsewidth-modulated inverter is filtered by an inductance–capacitance$(LC)$filter. The system states are estimated by means of an adaptive full-order observer, and no additional voltage, current, or speed measurements are needed. The rotor speed adaptation is based on the estimation error of the inverter output current. Quasi-steady-state and linearization analyses are used to design an observer that enables a wide operation region, including very low and very high speeds. A torque-maximizing control method is applied in the field-weakening region. Simulation and experimental results show that the performance is comparable to that of a drive without the$LC$filter.     

一种新的BLDCM无传感器梯形控制方法

无传感器无刷直流电机主要采用反电动势法进行控制,但该控制方法中的各种干扰会引起无传感器BLDCM的反电动势波形产生毛刺和纹波,这将导致对反电动势过零点的错误判断。文中提出了一种无传感器BLDCM的控制方法,使用提出的优选函数对反电动势过零检测结果进行滤波。通过分析反电动势波形并使用优选函数对反电动势过零点附近的毛刺脉冲和纹波进行数字滤波,避免了对反电动势过零点产生误判,得到了电机驱动电压的换相时刻,提高了对电机的精确控制。实验结果证明了新BLDCM无传感器梯形控制方法的正确性和可行性。     

ABR Flow Control Algorithm in ATM Networks:a Neural Network Approach

A Neural Network(NN) approach to ABR flow control algorithm in ATM networks is proposed. The NN predicts the queue length, its variation and possible cell loss, then regulates the source rate adaptively.Therefore, more appropriate value of the explicit rate can be determined in the corresponding field of the RM cells.This approach performs better than the traditional static feedback control. Additionally, the performance of this algorithm under CBR background traffic is discussed, and the simulatino results show that the neural network is also efficient.     

Anisotropy Comparison of Reluctance and PM Synchronous Machines for Position Sensorless Control Using HF Carrier Injection

Position sensorless control of reluctance and permanent magnet synchronous machines at zero and low speed is possible using HF voltage injection and proper demodulation. The so-called saliency position, which is tracked by the HF sensorless scheme, is different from the actual rotor position: the difference contains both offset and rotor-position-varying components, which may be explained by carefully considering the HF behavior of the machine and the effect that fundamental excitation and rotor position have upon it. This paper gives insight into the HF behavior of synchronous machines and serves as a practical guide for implementation of stable and robust position estimation at zero and low speed.      

Sensorless Control for a Switched Reluctance Wind Generator, Based on Current Slopes and Neural Networks

In this paper, the analysis, design, and implementation of a novel rotor position estimator for the control of variable-speed switched reluctance generators (SRGs) are presented. The rotor position is obtained using the unsaturated instantaneous inductance. This unsaturated inductance is estimated calculating the slope of the phase current and using a reduced-size neural network (NN) whose inputs are the average current and the saturated inductance. The proposed estimator requires less processing time than traditional methods and can be fully implemented using a low-cost DSP with very few additional analog/digital components. The rotor position estimator presented in this paper can be applied to a wind energy conversion system where the SRG is used as a variable-speed generator. This application is currently being studied because the SRG has well-known advantages such as robustness, low manufacturing cost, and good size-to-power ratio. Simulation and experimental results are presented using a 2.5-kW 8/6-SRG prototype.      

Decentralized Control of Dynamic Routing with a Neural Network Algorithm

A state-dependent routing algorithm based on the neural network model, which takes advantage of other dynamic routing algorithm for circuit-switched network, is given in [1]. But, the Algorithm in [1] is a centralized control model with complex O (N7), therefore, is difficult to realize by hardware. A simplified algorithm is put forward in this paper, in which routing can be controlled decentralizedly, and its complexity is reduced to O (10N3). Computer simulations are made in a fully connected test network with eight nodes. The results show that the centralized control model has very effective performance that can match RTNR, and the centralized control model is not as good as the centralized one but better than DAR-1.