Neuro-fuzzy speed tracking control of traveling-wave ultrasonic motor drives using direct pulsewidth modulation

The traveling-wave ultrasonic motor (TWUM) drive offers many distinct advantages but suffers from severe system nonlinearities and parameter variations, especially during speed control. This paper presents a new speed tracking control system for the TWUM drive, which newly incorporates neuro-fuzzy control and direct pulsewidth modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented by a low-cost digital-signal-processor-based microcontroller, hence reducing the system hardware size and cost. Experimental results confirm that the proposed speed tracking controller can offer good steady-state and transient performance.     

A speed identifier for induction motor drives using real-timeadaptive digital filtering

A novel sensorless speed identifier for real-time application in induction motor drives under steady-state and transient conditions is proposed. It is based on the calculation of rotor slot harmonic (RSH) frequencies using an adaptive digital filter. It outperforms other analog or spectrum-based RSH speed identifiers in terms of accuracy and speed of response. The new identifier measures the speed with less than 0.1% error by processing the stator current on a sample-by-sample basis. It is also capable of tracking speed transients of high slew rates with high accuracy. The authors believe this to be the first effective tracking of RSHs during transients ever reported. The proposed algorithm is computationally very efficient and requires only a single processor for its real-time implementation. Simulated and experimental data were used to validate the algorithm     

自适应神经模糊网络及其在位置伺服系统中的应用

针对一种旋转位置伺服系统,提出了基于T-S模型的自适应神经．模糊推理系统（ANFIS）的控制方法。文章讨论rANFIS网络的结构和学习算法,并在MATLAB的Simulink环境下,实现了对系统的实时控制。实验结果表明,洲练后的ANFIS能很好地控制实际的对象。     

基于自适应神经模糊推理系统的风电功率预测方法

对风电场输出功率进行精确的预测是保证含大规模风电电力系统安全稳定运行的重要手段。文中对多步滚动预测模式进行了分析,并建立了ANFIS(自适应神经模糊推理系统)预测模型,进而实现对风电功率的实时滚动预测。以吉林省西部某风电场的实测数据为例进行算例分析,其中在形成初始模糊推理系统结构时,采用的算法是减法聚类,该算法有效的避免了人工设定结构法产生的组合爆炸问题。将基于线性回归法、滑动平均法和持续法进行风电功率实时多步滚动预测时得到的预测结果与利用所提出的ANFIS预测方法得到的结果进行比较,结果表明后者的预测精度更高,进一步说明了ANFIS预测模型的有效性。     

感应电机无速度传感器控制自适应速度观测器

理论与实践证明自适应速度观测器是实现高性能的感应电动机无速度传感器控制系统的有效方法之一。由于该系统的非线性性质,在观测器设计以及系统应用中还有许多有待解决的问题。针对此类问题,从理论联系实际的角度,分析现有的电机磁链、速度观测器理论的要点及其成果,如建模的条件、自适应速度估计算法及其物理性质、系统稳定性分析、线性化方法、以及观测器参数的辨识等。同时,给出了实验结果、指出了这些内容中尚存的相关问题。     

ANFIS在短期负荷预测中的应用

为使负荷预测更精确,鉴于预测对象的不确定性和非线性,采用ANFIS预测电力系统短期负荷。ANFIS将模糊理论与神经网络融合,利用神经网络实现系统的模糊逻辑推理,采用混合学习算法调整前提参数和结论参数,自动产生模糊规则。该系统具有非线性映射和自学习能力,不基于数学模型,用独特的空间分层方法建立若干模糊推理系统,依靠专家经验获取控制信息,能用于负荷预测的非线性建模,获取负荷数据的最佳估计,克服数据处理过程中存在的不确定性和不完备性。所用ANFIS模型为2输出1输入5层1阶Sugeuo模糊系统。利用某局网负荷数据训练和检测ANFIS网络模型后预测负荷,结果表明该算法鲁棒性好,抗干扰能力强,能有效补偿对象的大纯滞后。     

A sensorless variable structure control of induction motor drives

In this paper, an indirect field-oriented induction motor drive with a sliding-mode controller is presented. The design includes rotor speed estimation from measured stator terminal voltages and currents. The estimated speed is used as feedback in an indirect vector control system achieving the speed control without the use of shaft mounted transducers. Stability analysis based on Lyapunov theory is also presented, to guarantee the closed loop stability. The high performance of the proposed control scheme under load disturbances and parameter uncertainties is also demonstrated via simulation examples.     

两种基于自适应神经模糊推理系统的风功率预测方法

随着风电渗透率的持续增长,风电功率预测的研究和应用变得非常重要,它将提高电网的安全性、稳定性和接纳能力。文中分别对基于风速预测和基于功率预测的两种风功率预测方法进行了分析,并建立了自适应神经模糊推理系统(adaptive neuro-fuzzy inference system,ANFIS)预测模型。利用吉林省西部某风电场的实测数据,基于ANFIS预测模型采用两种预测方法进行实时多步滚动预测,并与基于线性回归法、滑动平均法和持续法的风电功率实时多步滚动预测得到的预测结果进行比较,结果表明前者的预测精度更高,说明了ANFIS预测模型的有效性,并发现基于功率预测的ANFIS预测方法的精度要高于基于风速预测的ANFIS预测方法。     

Very low speed and zero speed estimations of sensorless induction motor drives

Recently, speed sensorless control of induction motor drives received great attention to avoid the different problems associated with direct speed sensors. However, low speed operation with robustness against parameter variations remains an area of research for sensorless systems. Stator resistance is of utmost importance for good operation of speed sensorless systems in low speed region. In this paper, a sliding mode current observer for an induction motor is presented. An estimation algorithm based on this observer in conjunction with Popov's hyper-stability theory is proposed to calculate the speed and stator resistance independently. The proposed speed observer with parallel stator resistance identification is first verified by simulation. Experimental results are included as well as to demonstrate the good performance of the proposed observer and estimation algorithms at very low and zero speeds.     

三相异步电动机转速偏低故障分析

简单分析了电动机不能启动机转速偏低故障的查找及排除方法。包括负载方面的原因,电动机本身原因及电源原因等。     

Stability analysis of five-phase induction motor drives with variable third harmonic injection

Five-phase induction motor drives have recently drawn attention among multiphase drives because the third harmonic injection (THI) allows obtaining both a higher output torque/volume relationship and broader stability margins. Previous studies have been focused on constant percentages of THI, requiring high and permanent injection if stability becomes main concern. This procedure is not optimal in the whole range of operation, degrading the drive performance. This paper proposes the use of a variable third harmonic injection (VTHI) control scheme that achieves similar robustness of constant third harmonic injection (CTHI) schemes but allows lower percentages of THI at light loads. A bifurcation analysis is presented for both constant and variable THI schemes, fully analyzing the steady state for different parameters conditions. Both the bifurcation analysis and the simulation of the proposed drive confirm the advantage of the proposed VTHI drive over existing CTHI schemes.     

Adaptive tracking controller for induction motor drives usingonline training of neural networks

This paper explores means of controlling the dynamics of the stator currents of an induction motor. A neural network-based identification and control scheme is presented. A single artificial neural network is trained to capture the nonlinear dynamics of the motor. A control law is derived using the dynamics captured by the network, and employed to force the stator currents to follow prescribed trajectories. The proposed architecture adapts and generalizes its learning to a wide variety of loads and, in addition provides the necessary abstraction when measurements are contaminated with noise. Extensive simulations reveal that neural designs are effective means of system identification and control for time-varying nonlinear systems, in the presence of uncertainty. The effects of parameter changes on the performance of the network is addressed. Particular emphasis is placed on the effects of sudden, random load torque changes. The difficulties addressed by this paper include incomplete system knowledge, nonlinearity, noise and delays     

MRAS-based speed sensorless control scheme with stator resistance identification function for vector-controlled induction motor drives

This paper describes a new scheme for the speed sensorless control of the vector-controlled induction motor drive. Based on the model reference adaptive system (MRAS) theory, the rotor speed of the induction machine is estimated with a full-order adaptive observer. The estimated speed then is used as the signal for the vector control and the speed control. To accurately estimate the speed at the lower speed range, the value of the stator resistance also is identified at the same time and then is used to modify the resistance value set in the observer. The resulting system is verified to be hyperstable with Popov's criterion. As a result, this drive can operate stably in a wide range of speed even at zero speed. Also, it is shown theoretically that neither speed nor resistance of the rotor can be identified at the same time because these two parameters are dependent on each other. Simulation results and experiments also show that the scheme is effective.     

基于自适应神经模糊推理系统的风功率缺失数据补齐

风电场风电输出功率数据的完整性在风能利用中具有重要意义。利用相邻风机法、持续法、平均插值法、标准功率曲线对应法均可以对风电缺失功率数据进行补齐,但都有各自的优缺点及适用范围;在单一补齐数据方法的基础上,文中采用自适应神经模糊推理系统(ANFIS)模型对丢失数据进行补齐和优化。对实测数据的仿真计算结果表明,用所提出的方法进行数据补齐后风电输出功率的计算结果平均相对误差降低,准确率提高。     

异步电动机变频调速系统的转矩脉动

针对异步电动机变频调速系统运行时出现的转矩脉动现象,在对已有调制模式进行研究的基础上,建立了计及逆变器死区时间后异步电动机系统的整体数学模型,对系统运行于不同调制模式下的转矩脉动进行研究,得出了改进梯形脉宽调制(MTPWM)模式减少转矩脉动效果最佳的结论。     

On innovative methods of induction motor interturn and broken-bar fault diagnostics

A fault indicator, the so-called swing angle, for broken-bar and interturn faults is investigated in this paper. This fault indicator is based on the rotating magnetic-field pendulous-oscillation concept in faulty squirrel-cage induction motors. Using the "swing-angle indicator," it will be demonstrated here that an interturn fault can be detected even in the presence of machine manufacturing imperfections. Meanwhile, a broken-bar fault can be detected under both direct-line and PWM excitations, even under the more difficult condition of partial-load levels. These two conditions of partial load and motor manufacturing imperfections, which are considered as difficult situations for fault detection, are investigated through experimentally obtained test results for a set of 2- and 5-hp induction motors.     

Adaptive and robust speed control of interior permanent magnet synchronous motor drives

This paper presents a robust speed controller for field oriented controlled interior permanent magnet synchronous motor (IPMSM) drives. The proposed controller is designed using integral variable structure control (IVSC) combined with linear quadratic regulator (LQR). The LQR scheme is used to decide the optimal feedback gain to shape the system dynamics by tuning the IVSC switching plane to guarantee the robustness of the control algorithm. The complete drive is implemented in real-time using digital signal processor (DSP) control board DS1102. The tracking properties and robustness of the proposed scheme are examined through both simulations and experimental work. It guarantees accurate control performance in the presence of parameter variations, step speed change and load disturbances. The performance of IPMSM drive system with a conventional proportional-integral (PI) controller is presented in comparison with the proposed controller. The results show a significant improvement in both the transient and steady state responses over the conventional PI controller.     

Five-level double converters for induction motor drives

In this article, a PWM control strategy for a three-phase, five-level double converter system is proposed. The proposed method is able to correct the five-level DC potentials, maintain the input power factor at near unity, and achieve adjustable-speed drive. Simulation and experimental results verified the validity of the control strategy. However, large voltage ripples were observed in the experimental results. Further study is needed to develop a control method that can reduce voltage ripples at the DC link capacitors as well as the harmonic distortions of the input and output waveforms