风力发电机组状态监测和故障诊断技术的应用研究

风力发电机状态监测是通过实时监测风力发电机运行状态,旨在发现潜在故障,预防事故的发生,进而提高风电设备的可靠性与安全性。由于风力发电机组长期运行在恶劣环境下,容易出现各类故障问题,为避免经济损失,保证风力发电机组稳定运行,做好实时状态监测和故障诊断至关重要。文章针对风力发电机组的运行以及故障处理等相关技术进行了分析,从发电机、齿轮箱、叶片、电气系统、液压传动系统状态监测和故障诊断几方面,研究了风力发电机组状态监测和故障诊断技术应用,以此确保整个系统安全稳定运行。     

基于状态码的风力发电机组可利用率计算

风力发电机组的可利用率是衡量风力发电机组可靠性的一个重要指标。目前国内使用可利用率计算方法各式各样,至今没有统一的标准。提出了一种基于风机现地控制系统状态码处理的可利用率计算方法,通过对状态码可利用属性的细化,实现了机组可利用率的精确计算。     

风力机状态监测与故障诊断技术研究

介绍了风力发电机组的基本构成,对风力机常用状态监测技术及主要测量参数进行了分析研究,并分析了风力机部件的常见故障,研究了部件的故障机理,最后,分析研究了适合于风力机的多种故障诊断方法,对国内外风力机状态监测、诊断技术和系统应用现状进行了概述。研究结果对保证风力发电机组安全运行,减少故障发生率,提高风力发电机组的运行可靠性．实现基于状态维修起到了指导作用。     

变速风力发电机变流器故障诊断方法

大型变距变速风力发电机组状态的监测与故障的诊断是保证机组长期稳定运行和安全发电的关键。文章针对变速风力发电机组中的变流器电路模型非线性强的特点,利用神经网络非线性映射特性,提出了采用基于波形直接分析的BP神经网络故障诊断方法。该方法能动态监视风力发电机变流器并网电路的工作状态,实时在线进行故障诊断和快速分析,确定变流器故障的部位和性质,可缩短风力发电机的故障停机时间。实际运行结果表明,该方法对变速风力发电机组的状态监测与故障诊断是有效的。     

风力发电机组的健康评估

为了保证风力发电机组的正常运行,降低运行维护成本,建立了风力发电机组的健康评估系统。机组健康评估要素包括机组监测、数据预处理、特征提取和专家库。机组健康评估是利用机组监测采集信息,经数据预处理后,进行特征提取;将提取的特征与专家库分析、比较,进而对风力发电机组的健康进行评估。通过对机组的健康评估,预先了解机组的健康状况,针对不同的故障提早预防或给出相应的处理措施,尽量排除故障或者防止故障再扩大。对风力发电机组的健康评估为风电场的状态检修提供了依据。     

变速恒频风力发电机组动态模型及并网研究

分析了风力发电系统的特点,着重介绍了变速恒频（Variable Speed Constant Frequency,VSCF）风力发电机组运行的基本原理．在此基础上,探讨了风力发电机组各部分的仿真建模,并分析了目前适用于不同条件下的双馈感应式异步发电机（Double—Fed Induction Generator,DFIG）变速恒频（AC—Exited Variable Speed Constant Frequency,AEVSCF）风力发电机的数学模型。对含变速恒频风机电网系统的故障扰动过程进行仿真分析．结果验证了建模的正确性。在故障扰动的最后对未来风电机组建模的研究重点提出了一些建议。     

模糊理论在风力发电机组故障诊断中的应用

模糊理论为基础，结合风力发电机组的实际运行工况、现场运行人员和专家的经验，分析了故障与征兆之间的模糊关系，形成了模糊故障诊断规则，建立了风力发电机组模糊故障诊断自适应修正数学模型。最后对一个具体故障实例加以分析，验证了模糊理论在风力发电机组故障诊断中的可行性。     

基于PSCAD的并网型风机建模及仿真

建立了包含风速模型、风力机模型、发电机模型和控制系统模型的风力发电机组的整体动态数学模型;应用PSCAD软件,以建立的数学模型为基础搭建了变速恒频风电机组仿真算例;并以短路故障和渐变风干扰为例,对由一台单机容量为2 MW变速恒频风电机组并网后的运行特性进行了仿真研究.仿真结果表明了变速恒频风电机组良好的运行特性及影响该机型风机稳定的因素.     

风电机组叶尖延长技术研究及应用

文中介绍了风力发电机组通过叶尖延长方案设计,选取某风电场三台风机进行叶尖延长改造,并对运行数据进行收集、整理处理,验证了叶尖延长技术对风电机组发电能力提升的理论可行性。     

基于FTA的风力发电机组液压系统故障分析及解决方案

风力发电机组液压系统对机组稳定运行起到至关重要的作用。针对某风力发电机组液压系统建立故障树模型,确定液压系统存在的各种失效模式及层级关系,在此基础上对各个失效模式的影响因素进行分析,针对主要故障给出了解决措施,为风力发电机组液压系统的设计及稳定运行提供参考。     

An engineering condition indicator for condition monitoring of wind turbine bearings

Condition monitoring (CM) of wind turbine becomes significantly important part of wind farms in order to cut down operation and maintenance costs. The large amount of CM system vibration data collected from wind turbines are posing challenges to operators in signal processing. It is crucial to design sensitive and reliable condition indicator (CI) in wind turbine CM system. Bearing plays an important role in wind turbine because of its high impact on downtime and component replacement. CIs for wind turbine bearing monitoring are reviewed in the paper, and the advantages and disadvantages of these indicators are discussed in detail. A new engineering CI (ECI), which combined the energy and kurtosis representation of the vibration signal, is proposed to meet the requirement of easy applicability and early detection in wind turbine bearing monitoring. The quantitative threshold setting method of the ECI is provided for wind turbine CM practice. The bearing run‐to‐failure experiment data analysis demonstrates that ECI can evaluate the overall condition and is sensitive to incipient fault of bearing. The effectiveness in engineering of ECI is validated though a certain amount of real‐world wind turbine generator and gearbox bearing vibration data.     

基于灰色粗糙集理论的风电机组传动链智能故障诊断方法

风电机组的状态监测和故障诊断是保证机组长期稳定运行和安全发电的关键。风电机组传动链系统的故障种类繁多,原因复杂,其故障征兆、故障原因和故障机理之间存在着极大的不确定性。文中在其故障诊断过程中,首先利用粗糙集原理对其特征参数进行约简,去除冗余参数,再利用粗糙集理论定量确定各特征参数的重要程度;根据约简的特征参数和各参数的重要程度,利用灰色关联度分析方法确定标准故障状态与目前机组状态的关联度,从而找到其故障之处。实例计算表明：在风电机组的故障诊断中将灰色系统理论和粗糙集理论结合是一种有效的方法,为其今后开展智能故障诊断提供了理论基础。     

基于机器学习算法与SCADA系统的风电机组变桨系统变频器的故障预警方法研究

变桨系统是风电机组的关键设备,但由于风电机组长期处于复杂的工作环境,导致变桨系统故障成为风电机组故障中最常见的故障之一,而变桨系统变频器故障在变桨系统故障中的占比很高.基于此,提出了一种变桨系统变频器的故障预警方案,分析SCADA系统数据,将机器学习算法应用于故障预警,并将模型温度残差作为故障预警的指标;然后,针对随机...     

基于深度卷积自编码器的风电机组故障预警方法研究

通过风电机组状态监测进行故障预警,可防止故障进一步发展,降低风场运维成本。为充分挖掘风电机组监控与数据采集（SCADA）各状态参数时序信息,以及不同参数之间的非线性关系,该文将深度学习中自动编码器（AE）与卷积神经网络（CNN）相结合,提出基于深度卷积自编码（DCAE）的风电机组状态监测故障预警方法。首先基于历史SCADA数据离线建立基于DCAE的机组正常运行状态模型,然后分析重构误差确定告警阈值,使用EMWA控制图对实时对机组状态监测并进行故障预警。以北方某风电场2 MW双馈型风电机组叶片故障为实例进行实验分析,结果表明该文提出DCAE状态监测故障预警方法,可有效对机组故障提前预警,且优于现有基于深度学习的风电机组故障预警方法,可显著提升重构精度、减少模型参数和训练时间。     

基于风电机故障的电力系统运营风险评估及保险转移的研究

风电机故障导致的系统电力缺额会给系统运营带来经济损失,目前由系统运营部门承担。为实现风险转移,建立了风电场风电机故障概率出力模型,并考虑了风电场风电机故障下电力系统运营的风险,通过对风电机故障条件下电力系统的蒙特卡罗抽样仿真,计算了不同负荷条件下电力系统运营调度的经济风险,并制定相应的保险转移机制。为全面评估风电的经济价值和风电场的规划设计提供了参考,并为风电场风电机故障风险转移提供了一种可供选择的方法。     

风电机组检修安全技术交底标准化建模实践

风电机组检修过程需具备较高的安全性和技术性,而现行的风电机组检修维护管理中安全技术交底不够全面、针对性不强且不具有过程性。为实现安全技术交底标准化和流程化,提出了基于故障树的PDCA模块化风电机组检修安全技术交底。实践结果表明,故障树分析标准化建模方法能够提升风电机组检修安全技术交底的完整性、彻底性及针对性;PDCA循环方法作为安全技术交底建模的有益补充,使检修管理系统具有更强的适应性、可用性、时效性及可扩展性。研究成果为风电机组安全生产及管理提供了新思路。     

Vibration‐based wind turbine planetary gearbox fault diagnosis using spectral averaging

Planetary gearboxes (PGBs) are widely used in the drivetrain of wind turbines. Any PGB failure could lead to a significant breakdown or major loss of a wind turbine. Therefore, PGB fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and lifespan of wind turbines. The wind energy industry currently utilizes vibratory analysis as a standard method for PGB condition monitoring and fault diagnosis. Among them, the vibration separation is considered as one of the well‐established vibratory analysis techniques. However, the drawbacks of the vibration separation technique as reported in the literature include the following: potential sun gear fault diagnosis limitation, multiple sensors and large data requirement, and vulnerability to external noise. This paper presents a new method using a single vibration sensor for PGB fault diagnosis using spectral averaging. It combines the techniques of enveloping, Welch's spectral averaging, and data mining‐based fault classifiers. Using the presented approach, vibration fault features for wind turbine PGB are extracted as condition indicators for fault diagnosis and condition indicators are used as inputs to fault classifiers for PGB fault diagnosis. The method is validated on a set of seeded localized faults on all gears: sun gear, planetary gear, and ring gear. The results have shown a promising PGB fault diagnosis performance with the presented method. Copyright © 2015 John Wiley & Sons, Ltd.     

基于数据融合的风电变桨系统故障预警研究

随着风电机组装机容量的不断攀升,同时带来并网发电率低、机组故障率高等缺点,导致风电机组整体利用率较低。为此提出一种基于数据融合的风电变桨系统故障预警方法。首先结合SCADA系统中的运行统计信息和历史数据,采用Relief特征参数选择方法筛选出与风电变桨系统故障相关的特征参数;然后采用数据融合的方法,建立基于MSET技术的风电变桨系统故障预测模型,并采用滑动窗口法进行故障预警阈值的确定;最后以上海某风场1.5 MW双馈异步风电机组进行实例分析,结果表明该方法可提前发现风电变桨系统故障征兆,实现对风电变桨系统的早期故障预警。     

Analysis and estimation of transient stability for a grid-connected wind turbine with induction generator

Increasing levels of wind energy in modern electrical power system is initiating a need for accurate analysis and estimation of transient stability of wind turbine generation systems. This paper investigates the transient behaviors and possible direct methods for transient stability evaluation of a grid-connected wind turbine with squirrel cage induction generator (SCIG). Firstly, by using an equivalent lump mass method, a three-mass wind turbine equivalent model is proposed considering both the blades and the shaft flexibility of the wind turbine drive train system. Combined with the detailed electromagnetic transient models of a SCIG, the transient behaviors of the wind turbine generation system during a three-phase fault are simulated and compared with the traditional models. Secondly, in order to quickly estimate the transient stability limit of the wind turbine generation system, a direct method based on normal form theory is proposed. The transient models of the wind turbine generation system including the flexible drive train model are derived based on the direct transient stability estimation method. A method of critical clearing time (CCT) calculation is developed for the transient stability estimation of the wind turbine generation system. Finally, the CCT at various initial mechanical torques for different dynamical models are calculated and compared with the trial and error method by simulation, when the SCIG stator terminal is subjected to a three-phase short-circuit fault. The results have shown the proposed method and models are correct and valid.     

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.