Small Signal Stability Analysis with Penetration of Grid-connected Wind Farm of PMSG Type

The impacts of large-scale grid-connected wind farm on direct-driven permanent magnet synchronous generator(PMSG)type are discussed on the small signal stability performances of power systems.Firstly,a simplified practical model of wind farm of PMSG type is derived for analyzing small signal stability.The rotor-fluxoriented control strategy is applied to the modelling of PMSG.Secondly,the framework of small signal stability analysis incorporating wind farm of PMSG type is built.Finally,the different simulation scenarios based on the IEEE 3-generator-9-bus test system as benchmark are designed to conduct the eigenvalue analysis and to assess the impacts of wind farm of PMSG type on power system small signal stability.Some conclusions are drawn with simulation results.

Probabilistic small signal stability analysis of power system with wind power and photovoltaic power based on probability collocation method

Recently, with increasing improvements in the penetration of wind power and photovoltaic power in the world, probabilistic small signal stability analysis(PSSSA) of a power system consisting of multiple types of renewable energy has become a key problem. To address this problem, this study proposes a probabilistic collocation method(PCM)-based PSSSA for a power system consisting of wind farms and photovoltaic farms. Compared with the conventional Monte Carlo method, the proposed method meets the accuracy and precision requirements and greatly reduces the computation; therefore, it is suitable for the PSSSA of this power system. Case studies are conducted based on a 4-machine 2-area and New England systems, respectively. The simulation results show that, by reducing synchronous generator output to improve the penetration of renewable energy, the probabilistic small signal stability(PSSS) of the system is enhanced. Conversely, by removing part of the synchronous generators to improve the penetration of renewable energy, the PSSS of the system may be either enhanced or deteriorated.

Small Signal Stability Analysis for a DFIG-Based Offshore Wind Farms Collected Through VSC-HVDC Transmission System

This paper modeled a doubly fed induction generator (DFIG) - based offshore wind farm integrated through a voltage source converter –based high voltage direct current (VSC-HVDC) transmission system, which is collected with infinite bus for small signal stability analysis. The control system of HVDC system is considered for the stability analysis. The impact of the VSC control parameters on the network stability is studied. The lineared dynamic model is employed to do small signal stability analysis by the eigenvalue analysis. The locus of the eigenvalue, which is corresponding to the oscillation model is studied. Time domain simulations conducted in Matlab/Simulink are used to validate the small signal stability analysis.

Small-Signal Stability Analysis of Wind Power System Based on DFIG

This paper focuses on the small-signal stability of power system integrated with DFIG-based wind farm. The model of DFIG for small-signal stability analysis has built;the 3-generator 9-bus WECC test system is modified to investigate the impacts of large scale integration of wind power on power system small-signal stability. Different oscillatory modes are obtained with their eigenvalue, frequency and damping ratio, the results from eigenvalue analysis are presented to demonstrate the small-signal stability of power system is enhanced with the increasing output of the wind farm.

Vibration Signals and Condition Monitoring for Wind Turbines

Rolling element bearings are critical parts of modern wind turbines as they carry the loads of the turning structure and the wind force. The stochastic nature of the wind loads makes it difficult to estimate the useful operational life of the bearings. Condition monitoring of these bearings in a real time environment could be very helpful in estimating their performance and in scheduling maintenance actions when a condition-based maintenance strategy is followed. This procedure can be successfully implemented by using vibration analysis in the time domain or in the frequency domain, giving useful results about the current condition of bearings and the location of potential faults. Permanently located transducers on proper positions on the bearings’ housings can be used in order to collect, process and evaluate real time measurements and provide information about the bearing’s performance. In this work, a test rig is utilized in order to evaluate the performance of rolling bearings. The results of the experimentation are satisfactory and the progress of fatigue failures can be predicted through vibration analysis techniques showing that implementation in real scale may be useful.

复合故障下风电齿轮箱声音信号耦合调制模型辨识与故障诊断

针对两级行星一级平行齿轮箱不同位置受损导致的复合故障,提出一种声音信号耦合调制模型,以辅助专家进行故障诊断。当风电齿轮箱发生复合故障时,其特征频率会以调幅和调频的形式影响不同轮系的啮合频率,为此,本文提出了复合故障下风电齿轮箱声音信号幅值耦合调制模型;利用模型参数辨识思路,确定所提耦合调制模型中不同轮系的调幅系数,并通过构建边带能量比指标,用于评价辨识效果;最后,利用声音信号耦合调制模型的重构谱,确定复合故障位置,实现具有辅助性质的故障诊断。实验与现场数据分析表明:用于评价辨识结果的边带能量比指标分别为0.948,0.972,0.977和0.9643,有效说明了模型辨识的有效性,为齿轮箱复合故障自动诊断奠定了基础。

基于改进残差网络的风电轴承故障迁移诊断方法

针对风电轴承故障源域数据和目标域数据特征分布不同而导致的故障诊断精度偏低问题,提出一种利用改进残差神经网络进行风电轴承故障迁移诊断的方法。该方法将卷积核和池化核设定为与一维振动信号卷积运算相适应的尺寸,从振动信号直接提取轴承的故障特征;在一维残差网络中同时使用批量归一化和实例归一化,进一步增强模型的特征提取能力;在模型训练阶段,通过源域数据和目标域数据的多核最大均值差异构建新的损失函数,以提高模型在不同分布数据集上的迁移学习及分类能力。利用故障轴承实验数据对方法的有效性进行验证,结果显示,即使受到轴承变转速运行工况和故障振动信号含噪声干扰成分的双重影响,该方法仍然可提取出轴承故障的重要特征,并实现不同工况轴承故障的迁移诊断和准确分类,这对于发展复杂环境下的旋转机械智能故障诊断技术具有参考价值。

基于微型数据采集器的一体化天平研制及应用

针对传统应变天平模拟信号传输方式存在抗干扰能力弱、传输距离短、线缆复杂等问题,开展了基于微型高精度数据采集器的一体化数字天平研制及应用。通过将微型高精度数据采集器嵌入测力天平,并且在微型高精度数据采集器中集成具有浮点运算能力的微控制单元,结合天平静态校准公式实现了天平实际力/力矩的实时解算和输出,并且实现了天平测量信号由模拟方式传输向数字方式传输的转变,提高测试系统的抗干扰能力。通过风洞内抗干扰测试表明,在常规高超声速风洞较为复杂的电磁环境下一体化天平信号传输几乎零干扰;通过开展高超升力体标模风洞试验验证表明,一体化天平试验重复性好(精度高),数据规律与常规天平测量结果一致,轴向力系数、法向力系数和俯仰力矩系数最大偏差分别为2.1%、1.8%和3.6%。

Detecting sand-dust storms using a wind-profiling radar

Sand-dust storm is a type of disastrous weather, typically occurring in arid and semi-arid climates. This study selected a region in the hinterlands of the Taklimakan Desert, called the Tazhong region, as the experimental area to quantitatively estimate the particle concentrations of sand-dust storms using the boundary layer wind-profiling radar. We thoroughly studied the radar echo signals and reflectivity factor features during the sand-dust storms. The results indicate that（1） under sand-dust storm conditions, boundary layer wind-profiling radar cannot capture the complete information regarding horizontal wind velocity and direction, but it can obtain the backscattering intensity of sand-dust storms; and（2） during sand-dust storms particle size distributions in the surface layer closely resemble log-normal distributions, with sand-dust particles sizes of 90–100 μm accounting for the maximum particle probability. Retrieved particle size distributions at heights of 600, 800, and 1000 m follow log-normal distributions, and the expected value of particle diameter decreases gradually with increasing height. From the perspective of orders of magnitude, the retrieved results for particle number concentrations and mass concentrations are consistent with previous aircraft-detected results, indicating that it is basically feasible to use boundary layer wind-profiling radar to quantitatively detect the particle concentrations of dust storms.

基于CALIOP双波长数据的海面测风信号筛选方法研究

基于海面后向散射模型,提出了一种利用CALIOP双波长海面信号进行数据质量控制的星载激光雷达数据筛选方法。选取CALIOP 2018年1―9月Version 4.1.0 level 1数据进行全球海面风速反演,为提高数据可利用率,在无云海域探测海面风速的条件下增加了可穿透云CALIOP数据,经过大气校正和双波长信号质量控制后进行风速反演,以准同步AMSR-2风速数据作为参考值进行检验。分别采用Cox-Munk和Hu模型的经验关系反演风速,对于1―3月数据,前者标准偏差分别为1.14、1.18、1.16 m/s,相关系数分别为0.90、0.91、0.91;后者标准偏差分别为1.17、1.19、1.17 m/s,相关系数分别为0.91、0.91、0.91。而相较于无云海域的反演研究,可利用数据量提高约45.5%,表明利用该方法既能保证反演精度同时又能有效提高数据可利用率,可为海面风场研究提供更多的数据源。

GNSS海洋反射信号接收机设计与分析

针对全球导航定位系统(global navigation satellite system,GNSS)海洋反射信号遥感技术的需求,文章设计了一种GNSS海洋反射信号接收机,用以对GNSS直射信号和反射信号进行接收。多普勒延迟映射结构是GNSS海洋反射信号接收机中的关键部件,采用直射信号通道闭环、反射信号通道开环的方式,反射信号通道利用直射信号通道获得的扩频码和多普勒频率,在半个C/A码片时间内进行相关积分,得到反射信号的相关峰波形。由于海面反射的GNSS信号是多个不同反射信号的集合,合成码的相关波形和直射信号不同,其位置和形状包含了海洋学参数信息,通过此相关函数波形即可解算出海面风场信息。GNSS海洋反射信号接收机技术可以为海洋环境监测、基础测绘、灾害监测等领域提供技术支持。

基于灰狼算法和极限学习机的风速多步预测

为了提高风速的多步预测水平,提出了一种基于数据信号分解和灰狼算法优化极限学习机的混合预测模型。首先,使用具有自适应噪声的完全集成经验模态分解算法将原始风速时间序列分解为若干本征模态函数和一个残差序列,并使用偏自相关函数法对模型输入进行特征选择;其次,在分解子序列上分别建立模型并进行预测,构造多输入多输出策略的极限学习机神经网络,使用灰狼优化算法求解其中的最优化隐含层权值和偏置;最后,对子序列进行重构并得到最终的预测结果。使用时间分辨率为15 min的多组实测资料开展模拟实验,所提模型在3个风电场的均方根误差分别为0.859、0.925、0.927 m/s,均低于其他对比模型,验证了该模型在未来4 h风速预测即16步预测中的有效性。

电力变压器绕组变形状态下的振动信号特性分析

近些年来国内电力变压器绕组机械故障频发,利用振动法监测电力变压器机械运行状态可以有效规避变压器故障风险。本文首先基于电力变压器绕组振动来源和加速度理论求解分析,搭建了110 kV电力变压器有限元模型,研究了变压器绕组在额定正常负载工况下的振动规律,发现了变压器在负载工况下绕组辐向振动强度远大于轴向振动,且绕组振动信号主频为100 Hz;在绕组上下端部固定约束条件下,其上下端振动强度小于绕组中间部位。然后,改变高压绕组几何形状使其发生鼓包变形,通过对比分析绕组未变形状态下的振动信号得出:当绕组发生形变时,其振动信号频谱中出现大量高次倍频谐波分量;随着绕组形变程度的加深,其振动信号主频不断增大,高次谐波分量分布也更为复杂。最后,通过定义基频占比、高频占比这两个特征参量,进一步分析了变压器绕组形变前后的振动特征变化规律,为实际现场利用振动法评估变压器绕组机械状态提供了依据。

基于传递函数分析的双馈风力发电系统动态建模方法研究

以双馈风力发电系统(doubly-fed induction generator based wind energy conversion system,DFIG-based WECS)为例,利用小信号分析法推导出由风机、双质量块传动链构成的动力与传动系统通用传递函数方程。动力与传动系统传递函数的零极点位置、稳定性与系统参数及系统运行工作点相关。该传递函数可进一步分解为扭转分量和非扭转分量。传动链参数仅对扭转分量造成影响。在此基础上,建立了考虑动力传动、电机、变流器、控制等环节的风力发电系统传递函数模型。模型综合了各环节参数,可直观反映系统参数对系统响应的影响,有助于深入了解系统动态行为。算例及时域仿真结果证明了所提出传递函数模型的准确性和高效性,可为系统参数设计研究提供理论依据。

利用双通道激光雷达验证低信噪比反演算法

针对相干测风激光雷达(LiDAR)在低信噪比(SNR)下反演算法的可靠性和精度难以验证的问题,搭建了一种双通道的脉冲相干测风LiDAR系统,可以同时获取高、低SNR的2组数据,并使用高SNR通道的结果作为真值,比较利用不同的算法在低SNR通道进行矢量风速估计的结果。该系统避免了使用其他观测设备作为真值时难以配准或观测目标不统一的问题。本文研究利用雷达获取的观测结果验证了各个算法的有效性,并在各个算法间进行了横向对比。最终,依据各个算法的性能表现和计算复杂度,指明了各个算法具有优势的使用场景。该结果对于充分利用现有系统、使用新算法提高低SNR下的数据获取效率,或在指定探测指标的前提下降低激光能量等硬件要求、实现系统小型化有一定意义。

基于5G技术的海上风电通信系统研究

[目的]海上风电项目众多设备位于海洋之上,交通不便导致运维检修难度大。海上设备信息的快速、无延迟、安全送达陆上显得格外重要。针对通信系统的特殊需求,文章提出了基于5G技术的海上风电场通信系统方案。[方法]首先建立信号覆盖面大、可靠性强的PTN+一体化小基站,再将5G一体化小基站和PTN网关融为一体,通过宽带PTN接入,实现快速、便捷的5G信号覆盖,并采用特定带宽的5G网络,提高通信系统的安全性。[结果]基于PTN+一体化小基站方式的5G通信系统建成后,实现了基于光传输的、支持多业务且利用特定带宽的IP传输。此系统可以帮助规划梳理各风机子系统,实现风机内部全方位信号覆盖,并可以将风机、海上升压站等设备运行状态及数据快速、安全的传输到陆上运维中心,解决风机多子系统、多业务实时安全传输困难的问题。[结论]研究的5G通信系统改善了海上风电通信,并利用特定带宽和新兴技术,实现了海上风电场的通信及时性和可靠性,提高了海上风电场海上运维人员的沟通效率,符合海上风电项目海上设备的通信需求,有望在工程中应用推广。

基于多源信号融合的风电机组状态监测系统设计

以投运风电场2 MW风电机组为主要工程目标,为实现其状态监测,提高服役性能,需要设计一套基于多源信号融合的状态监测系统。基于多源信号融合的状态监测系统能够克服单一振动信号难以准确评估风机的运行状态和服役性能的难题。重点对多源信号的类型进行了选择,根据多源信号的采集和处理方法、融合方法对系统的软硬件进行了设计。该系统对提高风力发电效益,预防风力发电机故障提供了工程指导。

基于改进MFCC算法的风力机叶片故障诊断方法

针对传统声信号特征处理方法无法有效提取叶片声音特征、导致叶片故障诊断准确率低的问题,提出一种基于改进梅尔频率倒谱系数(MFCC)算法的风力机叶片故障诊断方法。首先采用快速傅里叶变换(FFT)分析不同风速下叶片声音信号和风噪的频率特性,明确叶片声音信号的频率分布区域,将全频段分为三部分;然后采用粒子群优化算法(PSO)对梅尔(Mel)函数在不同频段上的敏感度进行优化,在迭代过程中将MFCC算法提取的叶片声音特征进行聚类,以轮廓系数作为适应度函数;最后基于支持向量机(SVM)构建分类器,实现风力机叶片故障的准确识别。以华北某风电场的叶片声音采集数据为算例,考察该算法在不同风速工况下的适应性,验证该方法的有效性。

基于EB-1D-TP编码算法的风电机组滚动轴承故障特征提取研究

滚动轴承运行状态检测是保证整个风电机组安全、稳定运行的关键,但由于风电机组所处的运行工况多变,难以准确识别滚动轴承的故障类型。为解决传统风电机组滚动轴承故障诊断需要大量采集信号、存在严重噪声干扰等问题,提出一种基于增强二进制1D-TP编码算法的故障特征提取方法。通过引入去“零”增强处理,提高不同状态下滚动轴承振动信号之间的区分度,并改进传统1D-TP编码算法,融合高、低两种模式,充分保留信号特征。最后通过对信号点和像素点之间相关性差异的分析,重新设定信号编码顺序。实验结果证明:该方法能够有效提高故障诊断系统对轴承振动信号特征的提取效果,增强不同特征之间的区分度,故障特征提取精度提升25%,且在提取效率上也表现出明显优势。

面向风电机组转速量测信号缺失的弹性控制策略研究

随着风能利用规模的不断增长,风电机组通信系统的脆弱性逐渐暴露,通信链路中时常出现一些故障或拒绝服务攻击,导致量测信号缺失。针对风电机组中转子转速量测信号缺失的问题,提出一种切换型弹性控制策略。首先,建立风电机组系统动态及故障/拒绝服务攻击模型;然后,设计神经网络观测器用于补偿故障/攻击造成的转子转速量测信号缺失;最后,设计切换型弹性控制策略以缓解故障/攻击影响,并基于FAST软件中1.5 MW风电机组进行实验验证。实验结果表明,所提弹性控制方案对于减轻故障/攻击影响、保证转子转速输出跟踪性能、延长风电机组正常使用时间具有显著的优势。