基于混沌优化PID风力发电机组变桨距系统研究

变桨距控制是永磁直驱同步风力发电系统在额定风速以上限制功率输出的主要控制手段,文章基于风力机功率输出特性曲线,分析了永磁直驱风力发电系统变桨距控制在全风况下对应的控制策略。在额定风速以上时利用转速和功率相结合的双闭环变桨距控制系统,并在传统的PID控制器的基础上运用了混沌优化技术,用来减小在额定风速附近引起的输出功率波动及载荷突变的不利影响。通过Matlab仿真软件对混沌优化变桨距进行验证,仿真结果表明该变桨距控制策略响应速度快且鲁棒性强。     

基于RBF神经网络整定PID的 风力发电变桨距控制

为了改善变速恒频风力发电系统在恒功率输出运行区域内的动态性能,在分析系统变桨距控制研究现状的基础上,基于RBF神经网络（RBFNN）整定PID控制理论设计风力发电系统变桨距控制器,建立了风力机及变桨距机构模型,以发电机转速测量值与额定转速相比后误差为输入设计控制器。在随机风作用下对设计的RBFNN整定PID控制器进行仿真,结果表明基于RBFNN整定PID控制理论的变桨距控制器具有良好的动态性能及对风速扰动的鲁捧性,能够有效改善风力发电系统变桨距控制效果。     

基于改进共轭梯度优化BP神经网络的风电机组变桨距控制

根据共轭梯度算法和传统BP神经网络的变桨距控制器的原理,针对兆瓦级风电机组变桨距控制设计了一种改进共轭梯度优化BP神经网络的变桨距PID参数自整定控制器,此控制器采用改进共轭梯度法修正BP神经网络的权值和阈值,实现BP神经网络变桨距PID控制器的在线整定。在Matlab/Simulink中仿真,仿真结果表明,采用此变桨距控制器可以在额定风速之上快速响应,在相同风速状况下使发电机桨距角调节命令更加准确,风轮转速更加平稳,输出功率维持在额定功率附近,取得了很好的变桨距控制效果。     

风力机变桨系统单神经元自适应PID控制

在分析风力发电系统能量转换理论的基础上,将单神经元自适应算法与常规PID算法结合,通过单神经元自学习,对PID控制器的系数进行在线调整,提出一种单神经元自适应PID调节变桨角度的控制方法,建立了风力机桨距角控制数学模型.风速超过额定值后,利用单神经元自适应PID控制器进行变桨控制,将控制结果与采用常规PID控制器的控制性能进行了对比.结果表明:单神经元自适应PID控制器可以将输出功率恒定在额定值,能够对变桨系统进行有效控制,达到预期的目标.     

基于模糊控制的风电机组独立变桨距控制

在额定风速以上时,通常采用变桨距控制技术调节大型风电机组来稳定其输出功率.由于风力发电系统的数学模型具有高度非线性、多变量、强耦合的特点,风速又具有多变性,因此文章在分析传统的PID变桨距控制技术优缺点的基础上,提出了基于三维模糊自适应PID控制的独立变桨距控制技术,并且引入风速的模糊前馈控制技术.对1 MW风电机组进行仿真,结果表明,在额定风速以上时,该方法不仅能稳定风电机组的输出功率,而且可以减小桨叶的拍打振动.     

改进灰狼优化算法在变桨距自抗扰控制中的应用

风电机组模型的不确定性以及风等外部干扰严重影响风电机组输出功率的稳定性,因此,将自抗扰控制器(ADRC)引入到风电机组变桨距控制中。当风速高于额定风速时,通过自抗扰变桨距控制策略有效调节桨距角,保证风电机组输出功率的稳定性。但ADRC参数繁多,仅靠专家经验进行整定比较困难。因此,文章提出将改进灰狼优化算法应用到ADRC中,完成参数的自寻优整定过程。仿真结果证明,经改进灰狼优化算法进行参数整定后的变桨距自抗扰控制系统能够对桨距角进行精确调整,并将输出功率快速稳定到额定值附近,具有较快的响应速度以及较好的抗扰动能力。     

基于模糊PID的风电系统转速控制仿真研究

由于风速具有随机性、不确定性、变化范围大等特点,风力发电机转速若采用传统PID控制,仅一组固定的参数难以在不同风速下均有好的控制效果。分析了风力发电系统各参数之间的关系,结合PID控制和模糊控制各自的特点,设计了模糊自适应PID控制器。在额定风速以下,该控制器用于改变发电机定子电压,从而改变发电机反力矩,调节转速,使得输出功率快速跟随风速变化。MATLAB/Simulink仿真结果证实其稳定性、动态速度响应均优于传统的PID控制,取得了较为理想的控制效果。     

基于功率灵敏度因子的风力机变速变桨距控制研究

针对大型变速变桨风力机在高风速区的气动性能随桨距角变化而改变的特性,文章提出了一种功率-桨距角变化的灵敏度控制策略。通过设计功率灵敏度因子调节PID变桨距控制器,建立输出功率偏差与风轮转速偏差的闭环系统。将提出的策略应用到某5 MW风机的参数模型中,利用MATLAB平台进行仿真验证。结果表明,提出的控制策略抑制了高风速区的扰动风速对系统的影响,使输出功率和风轮转速保持在额定值附近且波动很小,提高了系统的动态性能和稳态性能,同时提高了发电质量,并为风电机组并网需求奠定了理论基础。     

基于风轮气动特性的风力机变桨优化控制策略研究

当风速大于额定风速时,风电机组通过控制变桨机构调整桨距角来减小风能捕获,从而使机组的输出功率保持在额定功率附近。变桨系统一般采用PI(比例积分)控制算法,但由于风轮气动转矩与风速、风轮转速、桨距角呈高次复杂非线性关系,单一控制参数的变桨控制器难以满足风电机组在额定风速以上的运行性能要求。为了解决单一变桨控制性能不足的问题,提出一种基于风轮气动特性的风力机变桨优化控制策略,该策略通过测量桨距角当前值来动态调整变桨控制器参数,可有效提升变桨系统随风动作连续性,减小由变桨控制引起的转速与功率波动,削减机组由变桨动作引起的动态载荷。     

参与电网调频的风电机组线性变参数变桨控制策略

风电机组参与调频时其输出功率的调整将改变风电机组变桨动作的风速范围,同时由于桨距角调节气动功率的灵敏度随风况变化,使得传统PI变桨控制难以适用于风电机组参与调频时的复杂工况,出现风电机组转速振荡问题。提出一种基于线性变参数(Linear Parameter Varying, LPV)系统的风电机组变桨控制方法,对风电机组模型进行线性化,根据风速和桨距角的变化范围进行凸分解,得到其具有四面体结构的LPV模型,通过求解不同平衡点处的线性矩阵不等式(Linear Matrix Inequality, LMI)设计出相应的变桨控制器。仿真结果表明:与传统PI变桨控制相比,LPV变桨控制能有效减小转速的波动,降低低速轴载荷以及减小桨距角的波动程度,验证了该控制策略的有效性和先进性。     

A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change

A methodology is presented for downscaling General Circulation Model (GCM) output to predict surface wind speeds at scales of interest in the wind power industry under expected future climatic conditions. The approach involves a combination of Neural Network tools and traditional weather forecasting techniques. A Neural Network transfer function is developed to relate local wind speed observations to large scale GCM predictions of atmospheric properties under current climatic conditions. By assuming the invariability of this transfer function under conditions of doubled atmospheric carbon dioxide, the resulting transfer function is then applied to GCM output for a transient run of the National Center for Atmospheric Research coupled ocean-atmosphere GCM. This methodology is applied to three test sites in regions relevant to the wind power industry—one in Texas and two in California. Changes in daily mean wind speeds at each location are presented and discussed with respect to potential implications for wind power generation.     

Design of a wind turbine pitch angle controller for power system stabilisation

The design of a PID pitch angle controller for a fixed speed active-stall wind turbine, using the root locus method is described in this paper. The purpose of this controller is to enable an active-stall wind turbine to perform power system stabilisation. For the purpose of controller design, the transfer function of the wind turbine is derived from the wind turbine's step response. The performance of this controller is tested by simulation, where the wind turbine model with its pitch angle controller is connected to a power system model. The power system model employed here is a realistic model of the North European power system. A short circuit fault on a busbar close to the wind turbine generator is simulated, and the dynamic responses of the system with and without the power system stabilisation of the wind turbines are presented. Simulations show that in most operating points the pitch controller can effectively contribute to power system stabilisation.     

Transient and dynamic control of a variable speed wind turbine with synchronous generator

In this article, a controller for dynamic and transient control of a variable speed wind turbine with a full‐scale converter‐connected high‐speed synchronous generator is presented. First, the phenomenon of drive train oscillations in wind turbines with full‐scale converter‐connected generators is discussed. Based on this discussion, a controller is presented that dampens these oscillations without impacting on the power that the wind turbine injects into the grid. Since wind turbines are increasingly demanded to take over power system stabilizing and control tasks, the presented wind turbine design is further enhanced to support the grid in transient grid events. A controller is designed that allows the wind turbine to ride through transient grid faults. Since such faults often cause power system oscillations, another controller is added that enables the turbine to participate in the damping of such oscillations. It is concluded that the controllers presented keep the wind turbine stable under any operating conditions, and that they are capable of adding substantial damping to the power system. Copyright © 2007 John Wiley & Sons, Ltd.     

Simulating Feedback Linearization control of wind turbines using high‐order models

As wind turbines are becoming larger, wind turbine control must now encompass load control objectives as well as power and speed control to achieve a low cost of energy. Due to the inherent non‐linearities in a wind turbine system, the use of non‐linear model‐based controllers has the potential to increase control performance. A non‐linear feedback linearization controller with an Extended Kalman Filter is successfully used to control a FAST model of the controls advanced research turbine with active blade, tower and drive‐train dynamics in above rated wind conditions. The controller exhibits reductions in low speed shaft fatigue damage equivalent loads, power regulation and speed regulation when compared to a Gain Scheduled Proportional Integral controller, designed for speed regulation alone. The feedback linearization controller shows better rotor speed regulation than a Linear Quadratic Regulator (LQR) at close to rated wind speeds, but poorer rotor speed regulation at higher wind speeds. This is due to modeling inaccuracies and the addition of unmodeled dynamics during simulation. Similar performance between the feedback linearization controller and the LQR in reducing drive‐train fatigue damage and power regulation is observed. Improvements in control performance may be achieved through increasing the accuracy of the non‐linear model used for controller design. Copyright © 2009 John Wiley & Sons, Ltd.     

Probabilistic Wavelet Fuzzy Neural Network based reactive power control for grid-connected three-phase PV system during grid faults

This study presents a reactive power controller using Probabilistic Wavelet Fuzzy Neural Network (PWFNN) for grid-connected three-phase PhotoVoltaic (PV) system during grid faults. The controller also considers the ratio of the injected reactive current to meet the Low Voltage Ride Through (LVRT) regulation. Moreover, the balance of the active power between the PV panel and the grid-connected inverter during grid faults is controlled by the dc-link bus voltage. Furthermore, to reduce the risk of over-current during LVRT operation, a current limit is predefined for the injection of reactive current. The main contribution of this study is the introduction of the PWFNN controller for reactive and active power control that provides LVRT operation with power balance under various grid fault conditions. Finally, some experimental tests are realized to validate the effectiveness of the proposed controller.     

基于风速波动特征提取的超短期风速预测

针对风电场风速预测准确度不高的问题,提出一种基于风速波动特征提取的超短期风速预测方法。首先建立风速-风速变化量联合概率密度模型,分析风速的不确定性特征;根据风速波动特征,应用集合经验模态分解（EEMD）和风速分量样本熵（SampEn）值,将风速分解重组为波动量和趋势量;应用人工鱼群算法（AFSA）优化小波神经网络（WNN）进行趋势量预测;应用改进非线性自回归（INARX）神经网络对风速波动量进行预测,进而得到预测风速。通过实际风电场风速仿真预测,并与多种预测方法对比,表明该预测方法预测结果误差较小,可准确地进行超短期风速预测。     

风力发电机速度跟踪自适应控制研究

为风力发电系统设计了全程速度跟踪自适应控制器,以保证风轮机的转子转速在整个风速全程变化范围内都能迅速跟踪上给定的希望速度,希望的速度曲线是根据考虑了风速大小、转子所允许的最大转速和额定功率将风轮机转子转速划分为3个不同的运行区域给出的,在这3个区域中可保证风力发电机最大程度的获取风能,同时又可安全可靠运行.理论推导和仿真研究结果均表明,所设计的控制器能驱使闭环风力发电系统在整个运行过程中很好地跟踪所给定的速度曲线,从而实现了最大利用风能且安全运行的设计目的.     

A fuzzy logic pitch angle controller for power system stabilization

In this article the design of a fuzzy logic pitch angle controller for a fixed speed, active‐stall wind turbine, which is used for power system stabilization, is presented. The system to be controlled, which is the wind turbine and the power system to which the turbine is connected, is described. The advantages of fuzzy logic control when applied to large‐signal control of active‐stall wind turbines are outlined. The general steps of the design process for a fuzzy logic controller, including definition of the controller inputs, set‐up of the fuzzy rules and the method of defuzzification, are described. The performance of the controller is assessed by simulation, where the wind turbine's task is to dampen power system oscillations. In the scenario simulated for this work, the wind turbine has to ride through a transient short‐circuit fault and subsequently contribute to the damping of the grid frequency oscillations that are caused by the transient fault. It is concluded that the fuzzy logic controller enables the wind turbine to dampen power system oscillations. It is also concluded that, owing to the inherent non‐linearities in a wind turbine and the unpredictability of the whole system, the fuzzy logic controller is very suitable for this application. Copyright © 2006 John Wiley &Sons, Ltd.