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。     

双馈感应风力发电系统状态监测方法综述

在介绍双馈感应风力发电机组结构的基础上,分析了机组中容易发生故障的主要部件,指出了其监测重点,并对目前国内外状态监测方法的研究现状进行了总结,最后探讨了双馈感应风力发电系统状态监测的发展趋势和研究方向。     

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。     

基于虚拟仪器的水轮机组监测与诊断分析系统

随着水电机组在国家电力工业中的比重和水轮发电机组单机功率的增加,对水电机组的状态监测和故障诊断技术提出了更高的要求.采用虚拟仪器系统作为开发平台设计并组建水轮发电机组监测和故障诊断系统,该系统实现了水轮发电机组振动信号的数据采集、数据处理和分析,并进行故障诊断.     

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。     

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。     

并网型 75KW 风力发电机组的设计特点

介绍了并网型７５ｋＷ风力发电机组机械和电控系统的设计特点，并给出风力机组有关技术参数。     

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。     

基于混杂自动机的大型风力发电机组全程控制

风力发电系统是具有不确定性的复杂非线性系统,机组运行工况变化十分频繁。通过对风力发电机组的系统特性分析,发现风力发电系统呈现出混杂系统的典型特征。因此,基于混杂自动机理论建立了1.5 MW双馈型风电机组的混杂动态模型,并根据风力发电机组控制策略设计了能够在机组运行中实现全程自动化的混杂控制系统。仿真结果表明,基于混杂自动机的动态模型可以实现风电机组的全程模拟,所设计的混杂控制系统能够满足风电机组全程控制要求,证明了混杂系统理论应用于风力发电研究领域的有效性。     

风电机组故障智能诊断技术及系统研究

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。基于风电机组的基本结构,介绍了机组的故障类型和机理,论述了实际应用中机组的状态监测和故障诊断技术;基于BP神经网络的原理和优点,深入讨论了如何应用人工神经网络构建风电机组智能诊断系统,并给出了可行的系统设计方案和软件实现流程图。     

Development of a 20 kW wind turbine simulator with similarities to a 3 MW wind turbine

As the use of wind power has steadily increased, the importance of a condition monitoring and fault diagnosis system is being emphasized to maximize the availability and reliability of wind turbines. To develop novel algorithms for fault detection and lifespan estimation, a wind turbine simulator is indispensible for verification of the proposed algorithms before introducing them into a health monitoring and integrity diagnosis system. In this paper, a new type of simulator is proposed to develop and verify advanced diagnosis algorithms. The simulator adopts a torque control method for a motor and inverter to realize variable speed-variable pitch control strategies. Unlike conventional motor–generator configurations, the simulator includes several kinds of components and a variety of sensors. Specifically, it has similarity to a 3 MW wind turbine, thereby being able to acquire a state of operation that closely resembles that of the actual 3 MW wind turbine operated at various wind conditions. This paper presents the design method for the simulator and its control logic. The experimental comparison between the behavior of the simulator and that of a wind turbine shows that the proposed control logic performs successfully and the dynamic behaviors of the simulator have similar trends as those of the wind turbine.     

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.     

Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train

Some large grid connected wind turbines use a low-speed synchronous generator, directly coupled to the turbine, and a fully rated converter to transform power from the turbine to mains electricity. The condition monitoring and diagnosis of mechanical and electrical faults in such a machine are considered, bearing in mind that it has a slow variable speed and is subject to the stochastic, aerodynamic effects of the wind. The application of wavelet transforms is investigated in the light of the disadvantages of spectral analysis in processing signals subject to such stochastic effects. The technique can be used to monitor generator electrical and drive train mechanical faults. It is validated experimentally on a wind turbine condition monitoring test rig using a threephase, permanent-magnet, slow-speed, synchronous generator, driven by a motor controlled by a model representing the aerodynamic forces from a wind turbine. The possibility of detecting mechanical and electrical faults in wind turbines by electrical signal and particularly power analysis is heralded.     

风力发电机组振动故障分析技术

振动故障分析技术是风力发电机组预测性维护和降低维护成本至关重要的手段之一．文章介绍了当前应用于风力发电机组传动链的部分振动分析技术,以及这些振动分析技术的基本原理和优缺点。以期帮助振动分析者能够更好地利用振动状态监测系统分析和了解风力发电机组传动链的运行和振动状态．     

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。     

Wind turbine SCADA alarm analysis for improving reliability

Previous research for detecting incipient wind turbine failures, using condition monitoring algorithms, concentrated on wind turbine Supervisory Control and Data Acquisition (SCADA) signals, such as power output, wind speed and bearing temperatures, using power‐curve and temperature relationships. However, very little research effort has been made on wind turbine SCADA alarms. When wind turbines are operating in significantly sized wind farms, these alarm triggers are overwhelming for operators or maintainers alike because of large number occurring in a 10 min SCADA period. This paper considers these alarms originating in two large populations of modern onshore wind turbines over a period of 1–2 years. First, an analysis is made on where the alarms originate. Second, a methodology for prioritizing the alarms is adopted from an oil and gas industry standard to show the seriousness of the alarm data volume. Third, two methods of alarm analysis, time‐sequence and probability‐based, are proposed and demonstrated on the data from one of the wind turbine populations, considering pitch and converter systems with known faults. The results of this work show that alarm data require relatively little storage yet provide rich condition monitoring information. Both the time‐sequence and probability‐based analysis methods have the potential to rationalize and reduce alarm data, providing valuable fault detection, diagnosis and prognosis from the conditions under which the alarms are generated. These methods should be developed and integrated into an intelligent alarm handling system for wind farms, aimed at improving wind turbine reliability to reduce downtime, increase availability and leading to a well‐organized maintenance schedule. Copyright © 2011 John Wiley & Sons, Ltd.     

Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals

The implementation of condition monitoring and fault diagnosis system (CMFDS) on wind turbine is significant to lower the unscheduled breakdown. Generator is one of the most important components in wind turbine, and generator bearing fault identification always draws lots of attention. However, non-stationary vibration signal of weak fault and compound fault with a large amount of background noise makes this task challenging in many cases. So, effective signal processing method is essential in the accurate diagnosis step of CMFDS. As a novel signal processing method, empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing signal into mono-components under an orthogonal basis, which is seen as a powerful tool for mechanical fault diagnosis. Moreover, in order to avoid the inaccurate identification the internal modes caused by the heavy noise, wavelet spatial neighboring coefficient denoising with data-driven threshold is applied to increase Signal to Noise Ratio (SNR) before EWT. The effectiveness of the proposed technique on weak fault and compound fault diagnosis is first validated by two experimental cases. Finally, the proposed method has been applied to identify fault feature of generator bearing on wind turbine in wind farm successfully.     

金紫山风电场风机基础沉降观测与资料分析

结合金紫山风电场工程,介绍了金紫山风电场风机基础监测具体方法、监测时间和密度及监测数据分析。分析结果表明：风机基础在最初时期,由于风机基础本身自重、回填土重量、风机塔筒及风机机舱垂直荷载,呈现出下沉状态。沉降经过一段时间后,下降趋向稳定,风机基础沉降主要与主风向呈有规律变化,与主风同向的监测点上升,与主风反向的监测点下沉。