风轮机叶片电磁散射特性的占比分析与解析模型的建立

针对风轮机叶片雷达散射截面积的变化特性,分析了风轮机叶片雷达散射截面积（Radar Cross Section, RCS）对其整体雷达散射特性占比情况,实现对叶片解析模型适用范围的选取。考虑了风轮机叶片旋转平面与雷达视线（Line of Sight, LOS）夹角、叶片材料、叶片几何形状等因素对风轮机散射特性的影响,运用UG软件对风轮机叶片进行三维建模,利用真实叶片与相应简化圆柱叶片电磁散射特性的差异构建高保真的风轮机真实叶片电磁散射特性的解析模型,实现风轮机叶片RCS的快速计算。最后将解析模型计算结果与实测数据进行对比,验证了论文给出的真实叶片电磁散射特性解析模型的有效性。      

基于压电陶瓷的风机叶片模型覆冰主动监测

南方山区高湿低温严酷环境易导致冰灾,风机叶片覆冰会导致发电效率降低甚至停机,因此发展高效可靠的风机叶片覆冰监测技术对防冰、除冰具有重要意义。该文提出了一种基于压电陶瓷应力波测量的风机叶片覆冰主动监测方法。开展叶片模型进行模拟覆冰试验,在叶片表面布置伸缩型和剪切型两种压电陶瓷片,根据波动法原理对驱动器输入正弦扫频电压信号作为激励,记录不同传感器在不同结冰厚度下的响应信号,并对测量信号进行小波包能量分析。结果表明,同一压电陶瓷片接收信号的小波包能量与结冰厚度存在明显关系。     

基于散射中心的风电机叶片散射电场求解

为降低雷达系统的数据处理量及实现风电机实时目标特征识别,必须寻求风电机叶片散射电场的快速求解方法。针对传统电磁求解算法需处理巨量风电机叶片散射电场数据的问题,突破传统以超大电尺寸目标实体为基本单元进行广域空间电磁散射积分计算的思想,提出了一种基于散射中心的风电机叶片散射电场求解方法。基于非衰减指数和散射中心模型,通过距离-多普勒算法构建风电机叶片的雷达逆合成孔径图像,采用CLEAN 算法并结合相关系数提取风电机叶片的散射中心参数集,最终以散射中心产生的散射电场数据等效替代叶片本体的全部散射数据,实现了叶片散射电场的快速求解。以Vestas V82-1.65MW 型风电机为例,以矩量法计算结果为基准,文中所提方法计算准确度为93.20%,数据的压缩比达76.81,计算量的数量级比矩量法降低了106。     

大型风电叶片动态摄影测量网络优化

在大型风电叶片动态摄影测量中,为了对相机的站位进行优化,采用一种变异操作改进型遗传算法作为摄影测量网络优化方法,通过光线束前方交会的误差传递建立测量误差模型,以空间坐标测量误差的标准差为网络优化的目标,同时根据被测风电叶片几何结构和实际环境确定了相应的约束条件进行仿真实验,得到了最优的相机站位。结果表明,在以叶片长度为40m的风机为被测物的仿真实验中,最优站位的空间坐标测量误差标准差为2.7mm;通过对叶片长度为3.5m的风机模型进行实测实验验证,最优站位的相对测量误差为0.009%,最大误差为0.617mm。该研究为风电叶片摄影测量的网络优化提供了参考。     

风电叶片红外热波无损检测的实验探究

风电叶片是风电机组最关键的组件之一.随着风力发电机单台功率的不断提高,风轮叶片也越来越大,对质量可靠性要求也越来越高.但目前国内外尚无成熟的无损检测方法用于风电叶片检测.利用闪光灯脉冲激励红外无损检测方法对风电叶片制作过程中产生的几种典型缺陷进行了检测,取得了初步的实验结果.红外热波无损检测作为一种大面积、快速、非接触...     

预应力装配式叶片连接单元状态监测

提出了一种基于压电陶瓷激励与传感技术的预应力装配式风电叶片连接单元的状态监测方法。通过逐级施加螺栓预拉力对连接单元进行安装,再对连接段施加轴心拉力直至试件破坏,将压电陶瓷片粘贴在连接单元两侧分别作为驱动端和接收端,对受力全过程进行监测。比较不同激励信号下各阶段测量信号基于小波包分析的健康指标的变化,发现在连接界面脱开时信号出现明显衰减。该文的方法可实现装配式风力发电机叶片连接段状态的有效监测。     

大型风力机的噪声源产生机制实验研究

近年来,风力发电在国内得到了快速发展,但大型风力机产生的噪声也引起了广泛关注。该文通过远场传声器测量法开展了大型风力机噪声实验,研究了风力机的主要噪声源位置及产生机制。结果表明,在风力机的来流风的下游方向、风力机风轮叶片平面方向的噪声较大,存在声压级峰值的纯音基频和倍频,表明风力机的主要噪声源是叶片尖端气动噪声和风轮后面的机械噪声。最后指出了对这些主要噪声源进行有效控制的一些措施。     

Pitch controller for wind turbine load mitigation through consideration of yaw misalignment

Recent developments in sensor and actuator technologies have promoted the design and implementation of individual pitch controllers (IPCs) to mitigate fatigue loads on turbine blades caused by vertical wind shear. So far, IPCs have been designed assuming perpendicularity of the oncoming wind with respect to the turbine rotor plane as an independent yaw controller is dedicated to eliminate any misalignments. In this paper, a multi-input-multi-output (MIMO) IPC is designed based on the knowledge of mitigated blade load at a yawed inflow condition (i.e., yaw misalignment at certain angular position). Nonetheless, the proposed IPC is still to operate in the typical turbine configuration, in which the turbine is aligned with the wind direction. Performance of the proposed IPC is compared with that of the baseline collective pitch controller (CPC) and baseline IPC on simulations of the NREL 5 MW reference turbine at various turbulent wind conditions. Compared with the baseline CPC, the proposed controller is shown to contribute at least a 31.54% reduction in the blade out-of-plane fatigue load, a 35.32% reduction in the tower fore-aft fatigue load, and a 29.80% reduction in the tower side-to-side fatigue load.     

Innovative adaptive pitch control for small wind turbine fatigue load reduction

As a renewable source of energy, wind is widely used to produce electrical power. The progress of wind turbine technology can greatly benefit from the improvement of control algorithms. The pitch angle control of a horizontal axis wind turbine above the rated wind speed is a challenging issue related to the nonlinear aerodynamic behavior of blades. The linearization of aerodynamic model around nominal operating condition, as well as manufacturing deficiencies, result in unknown parameter uncertainties in a wind turbine model. Therefore, the performance of controller, which is designed based on the mathematical model, defects in practice. In the current paper, an adaptive self-tuning regulator (STR) configuration is proposed for the pitch control, so that the parameters of wind turbine model are constantly estimated and the controller gains are updated based on the assessed parameters. The STR structure consists of a recursive least square estimator and a proportional-integral-derivative (PID) controller with adjustable gains, which are determined by the pole placement method in a real-time routine. The robustness of the closed loop system is investigated by implementation of the control structure on an aero-servo-elastic wind turbine simulator. For the sake of comparison, a baseline gain scheduling PID controller, which is well-accepted for wind turbine pitch control, is designed. A comparison between the simulations of two controllers confirms a significant improvement in the closed-loop performance including less fluctuation of rotor speed and power besides minor fatigue loads on the blades and main-shaft.     

Health assessment and management of wind turbine blade based on the fatigue test data

With the quality problem becoming increasingly prominent, it is necessary to assess and manage the health condition of the wind turbine blade. The health degree is defined and calculated by the Grey Relation (GR) model to assess the health condition of the wind turbine blade quantitatively. The availability, reliability and artificial test result are taken as three indexes of the health degree. The availability is defined according to the stiffness degradation affected by the environmental temperature. Based on the health assessment results, after determining the decision objectives and the management strategies, the weights of the decision objectives and the health management decision are determined by the Analytic Hierarchy Process and Fuzzy (AHP-Fuzzy) decision method. This process is shown in a practical example of the 3 MW wind turbine blade. As a result, an approach and a certain theoretical guidance for the health assessment and management of wind turbine blade are proposed.     

The use of preview wind measurements for blade pitch control

Light detection and ranging systems are able to measure conditions at a distance in front of wind turbines and are therefore suited to providing preview information of wind disturbances before they impact the turbine blades. In this study, preview-based disturbance feedforward control is investigated for load mitigation. Performance is evaluated assuming highly idealized wind measurements that rotate with the blades and compared to performance using more realistic stationary measurements. The results obtained using idealized, “best case” measurements show that excellent performance gains are possible with reasonable pitch rates. However, the results using more realistic wind measurements show that without further optimization of the controller and/or better processing of measurements, errors in determining the shear local to each blade can remove any advantage obtained by using preview-based feedforward techniques.     

Effect of Impeller Solidity on the Generating Performance for Solar Power Generation

According to current solar power research, both the generating unit’s minimum start-up speed and power generation system’s minimum flow rate for operation decrease with the increase in the impeller solidity. Ideally, a high solidity should be achieved, as this translates more power for a solar power system in the start-up and shut-down cycles. However, increasing the number of blades does not increase the impeller solidity; therefore, there is an optimal number of blades needed to achieve the preferred solidity. This paper begins by selecting the blade airfoil and then performs a theoretical analysis based on the relationship between the blade number and chord length. Experiments are conducted to measure the starting and stopping wind speeds and power characteristics for different numbers of blades. The results show that a maximum impeller solidity of 0.2862 is achieved, as well as the minimum flow speed at the start-up, and the maintenance of the solar chimney power generation system is optimized when there are four blades.     

一种风机叶片图像采集及缺陷检测系统

本文针对目前风机叶片人工检测工作量大、效率低、缺陷检测准确率不高的问题,提出并设计了一种基于无人机图像的缺陷自动化检测系统。本文介绍了系统的图像采集系统、采集方法、缺陷检测原理及检测效果:系统以无人机为飞行载体实现了（风机叶片的）自动巡检,从而提高了巡检效率,降低了人工的工作量;通过图像分割及缺陷检测算法设计实现了缺陷可疑区域的自动检测;可见光加红外光双光融合提高了叶片缺陷自动识别的准确性。经过多次现场测试验证,本系统可以精确、快速地实现鼓包,裂纹和褶皱等缺陷的自动识别与检测。     

Extreme learning machine approach for sensorless wind speed estimation

Precise predictions of wind speed play important role in determining the feasibility of harnessing wind energy. In fact, reliable wind predictions offer secure and minimal economic risk situation to operators and investors. This paper presents a new model based upon extreme learning machine (ELM) for sensor-less estimation of wind speed based on wind turbine parameters. The inputs for estimating the wind speed are wind turbine power coefficient, blade pitch angle, and rotational speed. In order to validate authors compared prediction of ELM model with the predictions with genetic programming (GP), artificial neural network (ANN) and support vector machine with radial basis kernel function (SVM-RBF). This investigation analyzed the reliability of these computational models using the simulation results and three statistical tests. The three statistical tests includes the Pearson correlation coefficient, coefficient of determination and root-mean-square error. Finally, this study compared predicted wind speeds from each method against actual measurement data. Simulation results, clearly demonstrate that ELM can be utilized effectively in applications of sensor-less wind speed predictions. Concisely, the survey results show that the proposed ELM model is suitable and precise for sensor-less wind speed predictions and has much higher performance than the other approaches examined in this study.     

An empirical bispectrum model for sea surface scattering

The properties of a surface bispectrum are found by generating a skewed surface on a digital computer and then evaluating its correlation function, bicoherence function, power spectrum, and bispectrum. The bispectrum is defined to be the Fourier transform of the bicoherence function. It is found that the surface bicoherence function and its first and second derivatives must all vanish at the origin. In general, the surface bispectrum is a complex function. Its real part is centrosymmetric, just like the surface spectrum, and its imaginary part is antisymmetric. A function with the above-stated properties is introduced to represent the imaginary part of the sea surface bispectrum. The unknown parameter in this function is calibrated using a data set from the FASINEX experiment. The sea surface backscattering model is based on an integral equation model which accounts for frequency, polarization, incident angle, azimuthal angle, and wind speed. It is found that the proposed bispectrum can be used to account for the up/down wind asymmetry     

风轮机雷达散射特性仿真及微多普勒特征分析

风轮机复杂的电磁散射特性,会对其附近的空管通信、导航和监视等电子设备产生严重影响.研究风轮机的电磁散射特性,可为风轮机杂波检测和抑制提供理论依据,对保证空中交通安全具有重要的意义.论文首先基于风轮机散射点叠加的理论,考虑了雷达入射波到风轮机叶片和桅杆的初始相位以及入射波方位角和俯仰角对回波的影响,将单基地回波模型扩展到双基地模型.同时,在散射点叠加模型的基础上,提出了基于混合模型的风轮机散射特性分析.混合模型结合了散射点叠加模型和电磁仿真软件FEKO的优点,考虑了电磁波在叶片和桅杆上的反射系数等因素对回波的影响,可以实现任意观测点处的电磁散射特性计算及其微多普勒特征的分析.最后,分别对散射点叠加模型、FEKO以及混合模型的风轮机电磁散射特性分析方法进行了对比分析,给出了各自的优缺点及其适用场合.     

中国风电并网问题综述

随着传统能源的减少,开发利用可再生能源、实现能源的可持续发展成为世界各国能源发展战略的重要举措。风能作为可再生能源的重要方向,得到快速发展,但是仍存在许多问题,风电机组的并网问题首当其冲。造成此问题的原因是多方面的,文中主要从当前中国风电的并网现状、风电机组并网技术分析、风电并网对电力系统的影响以及如何解决风电机组的并网问题等方面入手,对中国风电并网中出现的问题进行综合分析,以促进风电产业的健康快速发展。     

大数据技术在风电机组运行状态监测评估中的应用

风电机组一旦因组件故障而停机，将耗费较长时间排故，给风电场带来较大的经济损失，因此需加强机组运行状态的实时监测，科学评估机组状态，以指导日常检修、维护等工作，有效降低机组发生故障的概率。在明确机组运行状态监测评估需求的基础上，引入大数据技术实现机组运行调度、故障处理等操作，需做好数据收集、分析和推理。把握大数据应用的技术思路，利用存储模块、收集模块、分析系统等加强技术实践应用，有效实现风电机组运行状态的监测评估，为解决风电机组运行管理问题提供有效的技术方案。     

飞机发动机旋转叶片的散射谱特征

飞机发动机旋转叶片引起的周期性运动部件调制(PMPM)影响目标多普勒频率跟踪精度,同时也为目标识别提供了重要特征信息。该文基于旋转叶片组电磁散射计算模型和实验测量数据,重点分析了PMPM频谱包络调制现象的形成原因。结果表明:与叶片倾斜角一样,扭角也是影响PMPM频谱分布的重要因素。     

An intelligent maximum power extraction algorithm for inverter-based variable speed wind turbine systems

This paper focuses on the development of maximum wind power extraction algorithms for inverter-based variable speed wind power generation systems. A review of existing maximum wind power extraction algorithms is presented in this paper, based on which an intelligent maximum power extraction algorithm is developed by the authors to improve the system performance and to facilitate the control implementation. As an integral part of the max-power extraction algorithm, advanced hill-climb searching method has been developed to take into account the wind turbine inertia. The intelligent memory method with an on-line training process is described in this paper. The developed maximum wind power extraction algorithm has the capability of providing initial power demand based on error driven control, searching for the maximum wind turbine power at variable wind speeds, constructing an intelligent memory, and applying the intelligent memory data to control the inverter for maximum wind power extraction, without the need for either knowledge of wind turbine characteristics or the measurements of mechanical quantities such as wind speed and turbine rotor speed. System simulation results and test results have confirmed the functionality and performance of this method.