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。     

风力发电机组状态监测系统设计与应用

风力发电机组状态监测系统的设计可以有效降低机组的检修维护费用,保障机组的安全稳定运行。对风力发电机组状态监测和故障诊断技术进行了深入的研究,设计了风力发电机组状态监测系统,并详细介绍了系统的结构与功能。通过系统在大型风力发电场的成功应用,验证了其对风力发电机组状态监测与诊断的有效性。     

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。     

基于主成分和神经网络的风力发电机主轴承故障预警

主轴承作为风力发电机组的重要部件,一旦发生故障,会影响风力发电机组整机工作的发电性能,严重时故障甚至会造成停机,不仅影响发电量,更会产生高昂的维修费用。通过运用相关性分析,根据Pearson相关系数矩阵对原有的多个指标进行分析。然后运用主成分分析,首先对数据的原始特征预处理,得到6个主成分,然后将这6个主成分作为BP神经网络的输入,运用神经网络对风力发电机的主轴承进行预警。神经网络模型结果表明,该模型对风力发电机主轴承故障预警具有非常好的识别效果,基于主成分和神经网络对风力发电机主轴承故障预警对实现机组智能故障诊断,提高机组的运行效率具有十分重要的意义。     

基于KF-PP分析的风电轴承故障模式识别

针对风力发电机组滚动轴承动态信号呈非正态、非线性,故障特征提取和初期故障诊断困难的问题,提出了基于核函数的投影寻踪分析故障模式识别方法。从轴承关键部位采集振动信号,经消噪预处理后,建立能够有效表述轴承运行状态的10特征指标数据空间,以基于核函数的投影寻踪方法构建故障评价体系,将待评估样本与评价体系进行对比分析,实现轴承故障的分类与识别。利用Matlab软件平台对实验数据进行仿真验证,结果表明,基于核函数的投影寻踪分析方法是一种有效的故障模式识别方法,能够实现轴承正常运行及故障类型的准确判断,具有良好的可靠性和可行性。     

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。     

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。     

大型风电场电网三相短路故障暂态分析

采用计算机仿真的方法,对大型风电场在电网发生三相短路故障时的暂态特性进行了研究.在首先建立了主动失速型风力发电机数学模型和仿真模型的基础上,研究了当装有失速型风力发电机组的风电场的外部电网发生三相短路故障时,风电场以及机组主要参数的暂态响应,深入分析了风力发电机组和配套的设备在电网故障时的动态特性.研究结果表明,三相短路故障引起的电压振荡为机组和电网的正常运行带来不利影响,常常会引起机组保护动作.     

基于变桨距控制策略的异步风电机组暂态稳定性研究

为了提高和改善在电网故障下并网风电机组的暂态稳定性,该文以并网笼型异步风力发电机组为例,考虑风力机传动链柔性带给机组振荡的影响,在典型变桨控制策略的基础上提出了一种增加以风力机转速为控制量的分阶段控制策略.通过建立并网异步风力发电机组的电磁暂态模型,基于Matlab/Simulink仿真平台,应用改进的变桨距控制策略,对电网三相对称短路故障下并网异步风力发电机组的暂态运行特性进行了仿真,并将其结果和多种不同变桨控制策略以及无功补偿策略的结果进行了比较.仿真结果验证了该文提出的变桨距控制策略能有效改善风力发电机组的暂态稳定性.     

基于FMEA法的风力发电机组故障模式分析

在分析风力发电机组设备构成的基础上,针对主轴及轴承、齿轮箱、发电机、偏航系统、变桨系统及塔架等机组主要设备,利用故障模式及影响分析法(FMEA法)开展了设备故障模式统计分析,获得了各设备故障模式详细统计报表,包括设备功能、功能故障、故障模式、故障原因、故障影响及预防措施等相关信息,为风力发电机组状态监测与故障诊断提供理论基础。     

Nonstationarity in correlation matrices for wind turbine SCADA-data

Modern utility-scale wind turbines are equipped with a Supervisory Control And Data Acquisition (SCADA) system gathering vast amounts of operational data that can be used for analysis to improve operation and maintenance of turbines. We analyze high-frequency SCADA-data from the Thanet offshore wind farm in the United Kindom and evaluate Pearson correlation matrices for a variety of observables with a moving time window. This renders possible a quantitative assessment of nonstationarity in mutual dependencies of different types of data. We show that a clustering algorithm applied to the correlation matrices reveals distinct correlation structures for different states. Looking first at only one and then at multiple turbines, the main dependence of these states is shown to be on wind speed. This is in accordance with known turbine control systems, which change the behavior of the turbine depending on the available wind speed. We model the boundary wind speeds separating the states based on the clustering solution. Our analysis shows that for high-frequency data, the control mechanisms of a turbine lead to detectable nonstationarity in the correlation matrix. The presented methodology allows accounting for this with an automated preprocessing by sorting new data based on wind speed and comparing it to the respective operational state, thereby taking the nonstationarity into account for an analysis.     

Wind turbine condition monitoring by the approach of SCADA data analysis

Wind turbines are being increasingly deployed in remote onshore and offshore areas due to the richer wind resource there and the advantages of mitigating the land use and visual impact issues. However, site accessing difficulties and the shortage of proper transportation and installation vehicles/vessels are challenging the operation and maintenance of the giants erected at these remote sites. In addition to the continual pressure on lowering the cost of energy of wind, condition monitoring is being regarded as one of the best solutions for the maintenance issues and therefore is attracting significant interest today. Much effort has been made in developing wind turbine condition monitoring systems and inventing dedicated condition monitoring technologies. However, the high cost and the various capability limitations of available achievements have delayed their extensive use. A cost-effective and reliable wind turbine condition monitoring technique is still sought for today. The purpose of this paper is to develop such a technique through interpreting the SCADA data collected from wind turbines, which have already been collected but have long been ignored due to lack of appropriate data interpretation tools. The major contributions of this paper include: (1) develop an effective method for processing raw SCADA data; (2) propose an alternative condition monitoring technique based on investigating the correlations among relevant SCADA data; and (3) realise the quantitative assessment of the health condition of a turbine under varying operational conditions. Both laboratory and site verification tests have been conducted. It has been shown that the proposed technique not only has a potential powerful capability in detecting incipient wind turbine blade and drive train faults, but also exhibits an amazing ability in tracing their further deterioration.     

基于SOM网的风电变流器故障诊断

我国新疆、甘肃、宁夏、内蒙、浙江、黑龙江、江苏、广东等都在大规模建设风电场,这些风电场建成后,其故障维护就有了很大市场.以新疆风电场为基础,尝试开发用于风力机故障智能诊断的系统.首先介绍了风力机及其变频器系统的结构,分析了变频器的故障机理.使用SOM神经网络对风机变流器进行了诊断,用数据验证了诊断结果.把传统的电力电子设备故障诊断技术与新疆风力机变频器的故障诊断相结合,为风电大面积推广应用产生了积极作用.     

风电机组状态检修的探讨

为了降低检修成本,提出风电机组状态检修。风电机组状态检修的内容包括数据采集、在线监测、故障诊断、故障预测、状态检修决策和实施。提出风电机组状态检修的两种模式：基于风电机组状态监测信号分析与特征提取的故障诊断检修模式;基于风电机组状态监测（含性能检测）及专家系统的智能化故障诊断与决策的检修模式。     

Challenges in the reliability and maintainability data collection for offshore wind turbines

Wind energy is abundantly available both onshore and offshore. As a response to the present climate crisis focus on wind energy is increasing due to its renewable and environmentally friendly characteristics. Due to social and political reasons the trend has been shifted largely from onshore to offshore wind farms. Offshore wind energy production faces a wide range of new challenges in design, development, manufacturing, installation, and maintenance and operation. The need, objectives, method, benefits, and application of a proposed reliability and maintainability database are identified in this paper. In the offshore oil and gas industry the OREDA concept for data collection has been running for more than 25 years. Therefore it will be briefly described what is considered to be the state of the art in this industry when it comes to data collection. Potential challenges and issues pertaining to the reliability and maintainability data collection of offshore wind turbines are outlined and categorized. The architecture of the proposed database is illustrated. The main building blocks of the database are briefly described and their possible effects on the reliability and maintainability of offshore wind turbines are highlighted. It is expected that the realization of the proposed database will open a new vista of knowledge in understanding the real behavior of offshore wind turbines in the marine environment. Another expectation is the benefits it will bring to the technological areas ranging from design to operation.     

考虑疲劳均衡的海上风电场主动尾流控制研究

海上风电场运行维护成本高,而其尾流效应影响更加突出,不但会影响风电场的发电效率,还会增大风电场内机组的疲劳载荷,增加运维成本。文章针对基于疲劳均匀的海上风电场主动尾流控制展开研究,通过GH-Bladed软件计算建立了风电机组在典型控制工况下关键零部件的疲劳损伤量数据库。其中的工况包括最大功率追踪、桨距角控制和偏航控制3种,并引用了量子粒子群算法,通过变桨和偏航两种方法进行优化控制,以实现海上风电场发电量提升和风电机组疲劳均匀的多目标主动尾流优化控制策略,降低海上风电场运维成本。仿真结果表明了所提出控制方法的可行性。     

Availability,operation and maintenance costs of offshore wind turbines with different drive train configurations

Different configurations of gearbox, generator and power converter exist for offshore wind turbines. This paper investigated the performance of four prominent drive train configurations over a range of sites distinguished by their distance to shore. Failure rate data from onshore and offshore wind turbine populations was used where available or systematically estimated where no data was available. This was inputted along with repair resource requirements to an offshore accessibility and operation and maintenance model to calculate availability and operation and maintenance costs for a baseline wind farm consisting of 100 turbines. The results predicted that turbines with a permanent magnet generator and a fully rated power converter will have a higher availability and lower operation and maintenance costs than turbines with doubly fed induction generators. This held true for all sites in this analysis. It was also predicted that in turbines with a permanent magnet generator, the direct drive configuration has the highest availability and lowest operation and maintenance costs followed by the turbines with two‐stage and three‐stage gearboxes. Copyright © 2016 John Wiley & Sons, Ltd.     

基于多状态空间划分的风电机组非完美维修决策

对风电机组的合理维护维修是减少风电场运维成本的重要方式。同一风电场的多台风力发电机构成了一个典型的多部件系统,各风力发电机的运行性能共同决定了系统整体的运行效率和维修需求。同时,对各风力发电机的维修效果也将影响到系统后续的可利用率和维修决策。该文以同一风电场中多台风力发电机的主轴组成的同型多部件系统为对象,在考虑非完美维修的条件下制定基于周期检测的视情机会维修策略;构建考虑非完美维修的多状态退化空间划分模型,以定义系统状态与维修需求的表示及关系,并归纳推导系统维修需求概率的计算模型和非完美维修干预下的系统退化及维修恢复过程中的状态转移概率;在此基础上,建立系统平均费用率解析模型,以确定最优的检测周期和维修阈值。通过某风电场的主轴实际运行数据进行数值实验,验证策略和模型的正确性和有效性,并对参数进行灵敏度分析以说明模型的适用性。结果表明该策略能有效减少风电场的运维成本。     

Analysis of the turbine standstill for a grid connected wind farm (case study)

In the present paper, several types of collected data were employed to analyse the causes of turbines shutdown in a grid-connected wind farm. Although the average availability of the considered wind farm exceeds 96%, the individual availability of some turbines does not exceed 92%. In this context, the present paper introduces a novel approach of understanding the turbine standstill and availability calculation. This approach is based on a variation of monthly energy production to weight the shutdown time including the maintenance and fault hours. The calm hours in summer are 60% less than the average calm time for the considered wind farm. The distribution of inoperative hours reveals a 300% difference between the original and weighed times of downtime. On the other hand, weighed times are used to assess the impact of various faults causing turbines shutdown. The frequency distribution of the faults has shown that 42% of turbine shutdowns are caused by network disturbances, 70% of them are attributed to grid disconnections. Finally, the time distribution of the network faults is investigated to illustrate their impact on the turbine standstill.     

Modelling the effects of environmental conditions on wind turbine failures

Operation and maintenance is one of the main cost drivers of modern wind farms and has become an emerging field of research over the past years. Understanding the failure behaviour of wind turbines (WTs) can significantly enhance operation and maintenance processes and is essential for developing reliability and strategic maintenance models. Previous research has shown that especially the environmental conditions, to which the turbines are exposed to, affect their reliability drastically. This paper compares several advanced modelling techniques and proposes a novel approach to model WT system and component failures based on the site‐specific weather conditions. Furthermore, to avoid common problems in failure modelling, procedures for variable selection and complexity reduction are discussed and incorporated. This is applied to a big failure database comprised of 11 wind farms and 383 turbines. The results show that the model performs very well in several situations such as modelling general WT failures as well as failures of specific components. The latter is exemplified using gearbox failures.