An effective control solution for doubly-fed induction generator under harsh balanced and unbalanced voltage sags

This paper presents a novel control algorithm for the rotor-side converter of Doubly-Fed Induction Generators. The main goal is to endow the system with effective Low Voltage Ride Through capability, under harsh balanced and unbalanced grid voltage sags, without relying on dedicated auxiliary hardware, which is commonly adopted to sustain severe line faults. In this respect, nonlinear control theory arguments are applied to design a controller capable of mitigating oscillations (particularly on rotor currents and voltages) arising during line faults, therefore preventing the system from disconnecting for protection. The proposed solution adopts both feedforward and feedback terms. The former stems from a thoughtful analysis of the system internal dynamics, taking into account the effects of line voltage perturbations, which is exploited to design feasible state trajectories for the generator electromagnetic variables. Specifically, such references do not contain poorly-damped oscillatory modes of the machine natural dynamics, expressed in synchronously rotating frames (such components turn into slowly varying DC ones in a stationary frames). Then, a state feedback unit is designed according to modern saturated control techniques, accounting for constraints on rotor voltage, and steering real variables toward references, where priority given to rotor currents, to avoid rotor-side converter tripping due to overcurrent. In addition, a non standard line voltage reconstruction and dip detection scheme, based on adaptive state observers, is designed, to reliably cope with challenging faulty conditions. Detailed numerical simulations validate the proposed method benefits under severe symmetric and asymmetric dip scenarios.     

双馈风电机组的自适应神经网络保性能虚拟同步机控制

近年来，可再生能源的电网渗透率逐年提升，电网对可再生能源参与一次、二次调频的需求也愈加紧迫，虚拟同步机技术（VSG）应运而生.VSG能够赋予新能源机组主动参与电网调频的能力，然而当VSG应用于双馈感应风力发电机（DFIG）时，存在动态过程中转子电流超出转子侧变流器（RSC）容量的风险.本文提出一种应用于DFIG的保性能虚拟同步控制器，通过使用误差映射函数将输出受限的系统转化为等价的不受限系统，并使用李亚普诺夫方法设计保性能控制器，保证转子电流在调频、故障穿越等强动态过程中不超过任意人为设定的限制；此外，利用神经网络自适应策略，对发电机组中的不确定动态特性进行补偿，从而获得理想的控制效果.最后，本文通过大量仿真验证了所提控制策略在调频能力、转子电流控制和应对参数偏差等方面的控制性能.     

HIERARCHAL CONTROL SYSTEM FOR A VARIABLE SPEED CAGE MACHINE WIND GENERATION UNIT USING NEURAL NETWORKS

A hierarchal control strategy, that addresses three control objectives for a wind generation system, is proposed in this paper. It controls the local bus voltage (to avoid voltage rise), captures the maximum power in the wind and also minimizes the power loss in the induction generator. In the first level, given the instantaneous wind speed, electrical torque and output power, the designed neural networks calculate the desired rotor speed, air‐gap flux and the grid side reactive power. In the second level, the desired current wave shapes (instantaneous three‐phase currents) of the rectifier and the inverter in a double‐sided PWM converter system are calculated. In the third level, the PWM controller guides the system towards the optimum operating conditions. Simulation results show that even as the wind speed changes randomly, the proposed control strategy leads the system to the optimum operating conditions.     

不平衡电网电压下双馈风力发电系统强励控制

针对电网电压不平衡造成的双馈风力发电机(DFIG)定子有功和无功功率振荡、电磁转矩脉动、定、转子电流不平衡等问题,提出了一种双馈风力发电系统转子侧变换器强励控制方案。推导了不平衡电网电压下DFIG定子有功、无功功率的二次谐波分量在同步旋转坐标系中的表达式,并以此为依据设计了电压强励补偿环。分析了有功、无功功率,电磁转矩及定、转子电流二次谐波分量之间的关系,采用单一强励补偿控制器对不同控制目标进行切换强励控制。对1.5MWDFIG系统的控制特性研究验证了所提控制策略的可行性和优越性。     

Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components. Shunt active filters (SAF) with current controlled voltage source inverters (CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents. However, CCVSI with traditional controllers have a limited transient and steady state performance. In this paper, we propose an adaptive dynamic programming (ADP) controller with online learning capability to improve transient response and harmonics. The proposed controller works alongside existing proportional integral (PI) controllers to efficiently track the reference currents in the d -q domain. It can generate adaptive control actions to compensate the PI controller. The proposed system was simulated under different nonlinear (three-phase full wave rectifier) load conditions. The performance of the proposed approach was compared with the traditional approach. We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison. The online learning based ADP controller not only reduced average total harmonic distortion by 18.41 %, but also outperformed traditional PI controllers during transients.      

一种新型的双馈电机的矢量控制方法

本文从原理上提出了一种通过检测转子侧的电流,控制转子电压与转子电流同相或反相的双馈电机的控制方法,通过检测转子电流,控制转子电压与转子电流同相或反相,控制转子侧功率因数为1,减小转子侧的变频器的容量,降低系统的成本.本论文对这一方案进行了分析,仿真和实验.     

Intelligent Open Switch Fault Detection for Power Converter in Wind Energy System

This paper proposes a simple and fast fuzzy logic-based open switch fault detection method for rotor side converter (RSC) in doubly fed induction generator (DFIG) wind turbine system. In the proposed scheme, only the mean values of the three-phase rotor currents are used to identify the power switch in which the open-circuit fault has occurred.

The wind energy conversion system model developed for the design and evaluation of the proposed fault detection technique including three principal controls. the first control ensure the regulation of the electromagnetic torque and the reactive stator power (named Rotor Side Converter (RSC) control), the second regulates the DC-link voltage at the desired level (named Grid Side Converter (GSC) control) and in order to achieve maximum power at any wind speed condition a maximum power point tracking (MPPT) control strategy has been used. The simulation model was developed in MATLAB/Simulink environment. The results show that the proposed fault detection scheme is able to rapidly and effectively identify open switch faults among other fault types in a time less than one period.     

Decentralized multivariable voltage and speed regulator for large-scale power systems with guarantee of stability and transient performance

This paper presents a novel procedure for the design of decentralized regulators for large power systems with a formal proof of ‘global’ stability. The distinctive feature of the solution is that both voltage and rotor speed dynamics are regulated simultaneously contrary to most of the solutions proposed so far in the literature. First, the traditional multimachine power system algebraic-differential equations are reformatted into suitable state equations, more appropriate for modern control tools. Secondly, a voltage and speed controller based on this model is proposed. The design consists of first cancelling some of the dynamical model non-linearities using non-linear excitation and valve input. The resulting subsystems are stabilized by auxiliary controls with linear and non-linear components. The non-linear component, which uses local signals to dominate those with interconnections, is derived from a stability criterion involving the Lyapunov function of the entire power system. The gains of the linear component are computed from the solution of an algebraic Riccati equation similar to the one involved in the full information H _∞ problem. These gains guarantee that effects of interconnection signals on voltage and speed dynamics are considerably reduced. The benefit of the proposed scheme is that the voltage regulation characteristic ensures a good post-fault voltage profile which helps improve rotor oscillations damping. Simulation results on a realistic power system confirm that the system stability is considerably improved in presence of severe contingencies.     

Self-sensing for electromagnetic actuators. Part II: Position estimation

The second paper in a two part series presents the position estimation scheme for an 8-pole heteropolar active magnetic bearing (AMB). The integrated self-sensing assembly comprises a nonlinear MIMO parameter estimator together with a coupled reluctance network model (Part I), by which problems associated with magnetic cross-coupling and saturation are collectively addressed. The parameter estimator utilizes the first harmonic components of the current and voltage waveforms to determine estimates for the x and y rotor positions. Magnetic saturation is accounted for using a saturation factor, which scales the demodulated coil currents to ensure that the actuator with the lowest flux density contributes the most to the position estimate. Basic functionality and feasibility of the proposed self-sensing scheme are demonstrated via an experimentally validated transient simulation model (TSM). The TSM incorporates magnetic effects such as eddy currents, cross-coupling, and hysteresis. In the second part of this work, the static and dynamic performance of the self-sensing sensor is evaluated. The influence of magnetic cross-coupling, saturation, and duty cycle variation on the position estimate is documented. The results demonstrate the importance of including a mutual coupling term in the position estimation model in order to stably suspend the rotor. Furthermore, stability margin analyses indicate that the robustness of the magnetic bearing control is satisfactory for unrestricted long-term operation.     

脊柱微创手术机器人运动控制系统的设计

设计了一套医疗机器人系统用于辅助脊柱微创外科手术操作。详细论述了这款5自由度脊柱微创手术导航机器人运动控制系统的设计与实现。提出了具有创新性的步进电机细分驱动模式,将电机定子电流从方波形状转变成为了正弦波形状,消除了步进电机的步间振荡现象。利用传感机构实现了对机器人末端工具的位姿反馈,并以此构成了对机器人末端工具的闭环控制。通过CAN实现了数字信号处理器之间的网络互联。最后对该系统进行实验,结果验证了控制系统具有良好的可靠性和稳定性。     

Intelligent control of doubly‐fed induction generator systems using PIDNNs

An intelligent control for a stand‐alone doubly‐fed induction generator (DFIG) system using a proportional‐integral‐derivative neural network (PIDNN) is proposed in this study. This system can be applied as a stand‐alone power supply system or as the emergency power system when the electricity grid fails for all sub‐synchronous, synchronous, and super‐synchronous conditions. The rotor side converter is controlled using field‐oriented control to produce 3‐phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field‐oriented control, is primarily implemented to maintain the magnitude of the DC‐link voltage. Furthermore, the intelligent PIDNN controller is proposed for both the rotor and grid side converters to improve the transient and steady‐state responses of the DFIG system for different operating conditions. Both the network structure and online learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Intelligent-controlled doubly fed induction generator system using PFNN

An intelligent-controlled doubly fed induction generator (DFIG) system using probabilistic fuzzy neural network (PFNN) is proposed in this study. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails for all sub-synchronous, synchronous, and super-synchronous conditions. The rotor side converter is controlled using the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field-oriented control, is primarily implemented to maintain the magnitude of the DC-link voltage. Furthermore, an intelligent PFNN controller is proposed for both the rotor and grid side converters to improve the transient and steady-state responses of the DFIG system at different operating conditions. The network structure, online learning algorithm, and convergence analyses of the PFNN are introduced in detail. Finally, the feasibility of the proposed control scheme is verified using some experimental results.     

不平衡电网电压下双馈感应发电机无源控制

本文以抑制电网电压不平衡所带来的定、转子不平衡电流为目标,提出了一种基于无源化方法的双馈感应发电机不平衡控制策略.首先在分析了正转同步旋转坐标系下的双馈感应发电机正序模型及负序模型无源性的基础上,设计了正、负序模型的无源性状态反馈控制器;然后给出了一种期望状态值计算方法,根据不平衡控制目标得到定、转子电流负序分量的指令值,结合定子电压的正序分量和电磁转矩的期望值计算出定、转子电流正序分量的给定值;最后得到电网电压不平衡条件下双馈感应发电机的无源控制器.仿真结果表明:所提出的控制方案有效地抑制了电网电压不平衡故障时定子及转子的不平衡电流,降低了输出电磁转矩的波动,提高了双馈感应发电机系统在电网电压不平衡条件下实现不间断运行的能力.     

An adaptive artificial immune system for fault classification

Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems. This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults. The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation. The algorithm consists of three steps. First, three-phase induction motor currents are measured with three current sensors and transferred to a computer by means of a data acquisition board. Then feature patterns are obtained to identify the fault using current signals. Second, the fault related features are extracted from three-phase currents. Finally, an adaptive artificial immune system (AAIS) is applied to detect the broken rotor bar and stator faults. The proposed method was experimentally implemented on a 0.37?kW induction motor, and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors.     

An improved model predictive control of low voltage ride through in a permanent magnet synchronous generator in wind turbine systems

This research investigates a wind energy conversion system based on a permanent magnet synchronous generator (PMSG). In addition, a model predictive control (MPC) is proposed for the PMSG in normal and fault conditions. The most efficient mode of the control algorithm is found for maximum power point tracking in normal conditions and fast dynamic response in fault conditions following the selection of the optimum voltage vector. This method prevents a sudden increase in the DC‐link voltage by storing the active power in the generator rotor inertia. Moreover, during the low voltage, the grid code adoption of the reactive current is injected into the grid side. The performance of the proposed control scheme is evaluated for a wind power generator using MATLAB software. The simulation results demonstrate that the proposed method can safeguard the DC‐link during the fault.     

BLDCM新型滑模观测器的无位置传感器控制

针对传统滑模观测器(SMO)抖振和相位延迟问题,提出了一种新型滑模观测器来获取无刷直流电机(BLDCM)反电动势(back-EMF),无需额外增加低通滤波器即能得到光滑的反电动势估计值.由反电动势估计值能够直接计算出转子位置角和转速.根据转子位置角和滞环控制器的输出来选择适当的电压空间矢量,实现无位置传感器控制,并根据转速和反电动势估计值计算转矩.最后,将该滑模观测器用于无磁链环直接转矩控制(DTC)中.实验结果验证了方案的可行性.     

Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines

In this paper, an adaptive neural network sensorless control scheme is introduced for permanent magnet synchronous machines (PMSMs). The control strategy consists of an adaptive speed controller that capitalizes on the machine’s inverse model to achieve accurate tracking, two artificial neural networks (ANNs) for currents control, and an ANN-based observer for speed estimation to overcome the drawback associated with the use of mechanical sensors while the rotor position is obtained by the estimated rotor speed direct integration to reduce the effect of the system noise. A Lyapunov stability-based ANN learning technique is also proposed to insure the ANNs’ convergence and stability. Unlike other sensorless control strategies, no a priori offline training, weights initialization, voltage transducer, or mechanical parameters knowledge is required. Results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions.     

A sliding-mode observer for high-performance sensorless control of PMSM with initial rotor position detection

This paper proposes a new method to detect the initial rotor position at standstill of permanent magnet synchronous motor (PMSM). To estimate rotor position and rotor speed from the back electromotive force (EMF) voltage, we apply sensorless speed control based on sliding-mode observer (SMO). The initial rotor position is detected by using a suitable high-frequency sequence of voltage pulses intermittently applied to the stator windings at standstill. With this approach, we managed to minimise the error on the estimated position to 3.75° electrical degrees without additional materials and uncomplicated calculations. The stability of the proposed SMO was verified using the Lyapunov method. Numerical simulations and experiments demonstrate that the novel SMO method can effectively estimate rotor position and speed with achievement of good static and dynamic performance. The experimental implementation is carried out on powerful dSpace DS1103 controller board based on the digital signal processor (DSP) TMS320F240.     

基于新型转子撬棒的双馈风电机组低电压穿越技术

针对目前双馈风力发电系统低电压穿越方案所存在的问题,尤其是转子侧投入撬棒后在穿越过程中的一些不足,提出了一种新型的阻容式转子撬棒以改善双馈风力机组的低电压穿越能力。在分析了双馈风力发电机故障期间投入撬棒后特点的基础上,结合其对应的等效电路,推导出双馈风力发电机定子侧与转子侧的无功功率表达式。对无功功率表达式定性分析后,得出提高低电压穿越能力的方案,并以此设计出一种不同于传统撬棒的新型转子撬棒,即阻容式转子撬棒。利用MATLAB/simulink搭建仿真模型进行验证,仿真结果表明:新型阻容式转子撬棒不仅可以在定子侧加快故障电压的恢复,而且可以限制转子侧过电流,抑制因撬棒的投切而对机侧变流器的暂态冲击,最终实现了有效提高双馈风力发电系统低电压穿越能力的目的。     

电网故障下双馈风力发电机网侧变换器的高阶滑模控制

针对电网故障下对双馈风力发电机网侧变换器的影响,本文采用滑模变结构控制方法,提出了采用增加瞬时功率前馈补偿的高阶滑模变结构控制策略,为此首先简要的分析了双馈风力发电机中双脉宽调制（PWM）型变换器的结构和控制原理,然后根据网侧变换器的数学模型,提出对网侧变换器采用电网电压定向欠量控制,以实现交流侧单位功率因数和直流环节电压控制．提出的方法提高了系统响应速度,克服参数变化和外电压波动的影响．仿真结果表明了该策略的有效性．