某风电场1.5MW风电机组齿轮箱点蚀及断齿故障分析

正齿轮箱是风电机组的重要部件,叶轮转速通过齿轮箱增速后使转速达到并网转速,有利于减少并网风电机组发电机的级数,缩小发电机的体积,提高机组效率。齿轮箱的损坏直接关系到风电场运行成本和收益。因受交变载荷的作用,在齿轮箱损坏的故障现象中,齿轮箱失效的主要形式是轮齿折断和齿面点蚀、剥落等,下面结合某风电场状况,对齿轮箱损坏的原因进行分析。     

基于温度参数的风电机组异常识别

文章针对风电机组运行过程中机组早期的异常状态识别问题,提出一种考虑有功功率的基于机组温度参数变化特性的风电机组异常识别模型。首先,分析风电机组各系统与温度相关的参数。然后,利用相关性理论,确定了与有功功率相关的温度参数:齿轮箱高速轴轴承前端温度、齿轮箱高速轴轴承后端温度、齿轮箱油温、发电机驱动端轴承温度、发电机非驱动端轴承温度、发电机定子绕组温度,形成了异常检测的参数体系。再次,以正常状态下机组温度参数的偏度和峰度的最大区间作为阈值,建立风电机组异常识别模型。最后,采用滑动窗口对机组运行状态进行在线监测。通过实例研究发现,当机组发生异常状态时,温度参数的偏度或者峰度超过了阈值,比警报提前了15 d。该识别模型为风电机组的早期故障预警提供了参考。     

基于IPSO神经网络的风电机组主轴状态监测

风电机组主轴是叶轮和齿轮箱的连接部分,在机组传动链中具有传递转矩和能量的作用,因此对主轴进行状态监测关系到风电机组的稳定性。将改进粒子群算法(IPSO)与BP神经网络相结合构造主轴温度模型并进行预测。当主轴发生故障时,模型输入的观测向量发生异常变化,导致模型预测残差发生改变。为提高主轴异常预警的灵敏度和可靠性,文中采用基于莱依特准则的双滑动窗口对预测残差序列进行实时的统计分析,如果残差均值或标准差超出设定的故障报警阈值时,发出报警信息。     

基于确定性随机子空间方法的风机齿轮箱故障预警

齿轮箱故障是造成风电机组停机时间最长的一种故障,对其故障进行早期预警,对保证整机的可靠运行和减少维修费用具有重要意义。文章提出了一种基于确定性随机子空间方法的齿轮箱故障预测算法,首先,该算法利用齿轮箱正常状态的实时监测振动和转速数据,建立齿轮箱的状态空间模型,并得到一组参考特征值;然后利用这组参考特征值与实际监测数据所求特征值进行比较,利用均方根误差(RMSE)作为齿轮箱故障预警指标,并结合统计过程控制原理定义该指标的门槛值,来实现对齿轮箱运行状态的监控。通过对实际监测数据的仿真验证,表明了所提方法的正确性和有效性。     

一种基于非线性偏最小二乘的风电机组齿轮箱状态监测方法北大核心CSCD

风电机组状态监测是提升机组运行水平和经济效益的重要手段。文章提出了一种基于非线性偏最小二乘(PLS)的风电机组齿轮箱状态监测方法,利用数据采集与监控系统(SCADA)数据对齿轮箱油温进行建模和监测。首先,基于无监督聚类对SCADA数据进行预处理,利用相关性分析选取与齿轮箱油温相关的输入变量;然后,构建用于表征非线性关系的输入变量,建立正常运行工况下齿轮箱油温的非线性PLS模型;最后,根据模型输出结果与齿轮箱油温的残差分布,设置合理阈值,用于齿轮箱状态监测。应用该模型对某大型风电机组齿轮箱进行状态监测。监测结果表明,相比于BP神经网络模型,该模型具有更高的拟合优度和预测精度。     

关于风机预警分析的可行性研究

文章研究的风机预警分析为预先判断机组部件是否出现故障提供了可能,它从大部件预警、基于统计规律的预警、功率曲线预警、CMS振动检测预警、损坏件预警、运行分析预警等6个方面进行了可行性分析,通过综合机组部件历史故障前的数据,以及它们的运行原理等相关理论依据,通过数据挖掘找出部件正常运行的数据规律,以及机组部件故障前一段时间的数据规律.当该部件再次出现异常数据时,机组会自动预警,现场人员可立即检查部件是否出现故障.防患于未然,降低故障率,降低成本,保障风机正常可靠运行.     

一种基于区间划分的风机故障预警方法

针对大型风电机组运行工况复杂多变,仅依靠恒定温度值作为变桨装置是否预警的触发指标,容易造成不报、误报及排查时间不足等问题的现象,提出了一种基于环境温度和风速等参数进行区间划分的在线故障预警方法。首先,对各子区间依据显著性差异分析方法设定不同的预警阈值;然后,将实时数据代入相应子区间判断是否异常;最后,采用滑动窗口方式计算异常率,并结合现场经验设置异常率预警上限作为触发预警指标。经过现场验证,将该分析方法用于河北冰峰风场1.5 MW风机变桨系统故障预警。结果表明,该方法能够准确预测风机状态变化趋势,实现变桨装置故障预警,降低风电机组运行和维护成本。     

多参数模型风电机组叶片结冰监测与预警研究

详细分析叶片结冰对风电机组运行性能和运行参数的影响,采用功率、叶轮转速和环境温度作为监测叶片结冰的变量.采用高斯过程回归分别建立功率模型和叶轮转速模型实现2个参数的实时监测.引入序贯概率比检验方法分析功率和叶轮转速模型的预测残差以发现2个参数在叶片结冰时的异常变化.当风电机组功率异常、叶轮转速异常且环境温度在0℃附近这...     

基于轴承温度的风机齿轮箱故障预警研究

齿轮箱轴承作为能量传递的关键部件,对风机状态评估具有重要意义。文章针对齿轮箱故障,提出了基于改进的粒子群算法(APSO)优化的BP神经网络齿轮箱轴承温度预测模型。首先,基于主成分分析法,选取SCADA系统中影响齿轮箱轴承温度的参数,建立正常状态下的齿轮箱轴承温度预测模型,通过对轴承温度残差值进行分析,得出风机故障告警和报警阈值,从而实现对齿轮箱故障的有效预警;最后,利用华北某风电场的数据进行实验仿真,对比分析粒子群(PSO)优化的BP神经网络预测模型和传统BP神经网络预测模型。结果表明,提出的预测方法拥有精度高、收敛速度快等优点。     

基于改进集成KNN回归算法的风电机组齿轮箱状态监测

风电机组齿轮箱是容易发生故障的重要部件,维修费用高昂,因此有必要对其进行实时状态监测。针对集成K近邻(KNN)算法对随机采样不敏感的问题,提出了一种基于规则采样的改进集成KNN模型。首先利用距离相关系数进行变量选择,然后基于正则化互信息对变量进行排序,将其用于规则采样,构造子训练集,最后基于统计过程控制方法设置预警阈值对实时残差进行分析,根据健康度曲线对风电机组齿轮箱健康度进行监测,并利用某风电机组实际数据对所提方法进行验证。结果表明：所提方法显著提升了模型估计精度,该模型优于常规集成KNN模型,可以实现齿轮箱的早期故障预警。     

基于网格搜索优化ERF模型的风电机组异常状态预警

提出一种基于网格搜索优化(GS)极端随机森林(ERF)模型的风电机组性能预测及异常状态预警方法.首先,采用离散度分析法清洗噪声和异常工况数据,以获取建模用正常运行状态数据.其次,通过分析风机运行与控制原理,选取与转速和功率具有较高相关度的特征参数作为模型输入,完成预测模型训练和验证,并对比ERF模型与其它几种模型的建模...     

Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring

A review of current progress in Condition Monitoring (CM) of wind turbine gearboxes and generators is presented, as an input to the design of a new continuous CM system with automated warnings based on a combination of vibrational and Acoustic Emission (AE) analysis. For wind turbines, existing reportage on vibrational monitoring is restricted to a few case histories whilst data on AE is even scarcer. In contrast, this paper presents combined vibration and AE monitoring performed over a continuous period of 5 days on a wind turbine. The vibrational and AE signatures for a healthy wind turbine gearbox and generator were obtained as a function of wind speed and turbine power, for the full normal range of these operational variables. i.e. 5–25 m/s and 0–300 kW respectively. The signatures have been determined as a vital pre-requisite for the identification of abnormal signatures attributable to shaft and gearbox defects. Worst-case standard deviations have been calculated for the sensor data. These standard deviations determine the minimum defect signal that could be detected within the defined time interval without false alarms in an automated warning system.     

海上风电结构横风向气动力阻尼特性研究

基于横风向气动力阻尼理论计算模型,以NREL-5 MW海上风电机组为例,对其运行过程中横风向气动力阻尼进行计算,并采用FAST软件对计算结果进行验证。之后,研究转速、叶片桨距角和运行方式对横风向气动力阻尼的影响。研究结果表明：NREL-5 MW海上风电机组结构运行状态下的横风向气动力阻尼在0%~0.8%范围内变化,其随风电机组运行转速及叶片桨距角的增大而增大;此外,海上风电机组不同运行方式对其横风向气动力阻尼也会产生较大影响。     

Experimental identification of modal parameters of an industrial 2‐MW wind turbine

This contribution presents modal testing of a 2‐MW wind turbine on a 100‐m tubular tower with a 93‐m rotor developed by W2E Wind to Energy GmbH. This research is part of the DYNAWIND project of the University of Rostock and W2E. Beside classical modal analysis schemes, this contribution mainly focusses on the application of operational modal analysis techniques to a wind turbine. Specific problems are addressed, and hints for modal testing on wind turbines are given. Furthermore, an effective measurement setup is proposed for identification of the modal parameters of a wind turbine. The measurement campaign is divided in two parts. First, a measurement campaign using 8 sensor positions on a rotor blade was done while the rotor is lying on ground. Second, a detailed measurement campaign was done on the entire wind turbine with the rotor locked in Y position using 61 sensor positions on the tower, the mainframe, the gearbox, the generator, and the low‐voltage unit. While the rotor blade was tested by classical and operational modal analysis techniques, the entire wind turbine was tested by operational modal analysis techniques only. The mode shapes and eigenfrequencies of the wind turbine identified within the measurement campaigns are within the expected range of the design values of the wind turbine. But in contrast, the damping ratios differ strongly from those given in guidelines and literature. Furthermore, a strong influence of aerodynamic damping compared to structural damping is observed for the first tower mode even for a parked wind turbine.     

基于油液光谱监测的风电机组齿轮箱磨损状态的研究

风电机组齿轮箱的磨损微粒主要是铁颗粒,铁颗粒含量的增长趋势能直接反映出风电机组齿轮箱的磨损状态。以Spectro油液光谱分析仪监测风电机组齿轮箱在用齿轮油中的铁元素含量,通过一段时间内铁元素的增加量和风电机组可利用小时数,可计算得到单位可利用小时数下的铁元素增加量ΔQ_Fe;引入可靠性理论研究了ΔQ_Fe的分布规律,并以风电机组齿轮箱在用齿轮油的监测数据为依据,建立基于ΔQ_Fe的齿轮箱磨损阈值模型。在大样本数据的基础上建立的磨损阈值模型能够更准确地分析风电机组齿轮箱的磨损状态趋势,可为风电机组齿轮箱磨损状态评估提供参考依据。     

Monitoring wind turbine gearboxes

Concerns amongst wind turbine (WT) operators about gearbox reliability arise from complex repair procedures, high replacement costs and long downtimes leading to revenue losses. Therefore, reliable monitoring for the detection, diagnosis and prediction of such faults are of great concerns to the wind industry. Monitoring of WT gearboxes has gained importance as WTs become larger and move to more inaccessible locations. This paper summarizes typical WT gearbox failure modes and reviews supervisory control and data acquisition (SCADA) and condition monitoring system (CMS) approaches for monitoring them. It then presents two up‐to‐date monitoring case studies, from different manufacturers and types of WT, using SCADA and CMS signals. The first case study, applied to SCADA data, starts from basic laws of physics applied to the gearbox to derive robust relationships between temperature, efficiency, rotational speed and power output. The case study then applies an analysis, based on these simple principles, to working WTs using SCADA oil temperature rises to predict gearbox failure. The second case study focuses on CMS data and derives diagnostic information from gearbox vibration amplitudes and oil debris particle counts against energy production from working WTs. The results from the two case studies show how detection, diagnosis and prediction of incipient gearbox failures can be carried out using SCADA and CMS signals for monitoring although each technique has its particular strengths. It is proposed that in the future, the wind industry should consider integrating WT SCADA and CMS data to detect, diagnose and predict gearbox failures.Copyright © 2012 John Wiley & Sons, Ltd.     

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。     

基于长短时记忆网络融合SCADA数据的风电齿轮箱状态监测

针对不具有时间记忆能力的机器学习方法融合风电机组数据采集与监控系统(SCADA)的时序数据而导致风电齿轮箱状态预测精度不高的问题,提出基于长短时记忆(LSTM)网络融合SCADA数据的风电齿轮箱状态预测模型。选择能表征风电齿轮箱运行状态的某个监测量作为模型的输出量,基于灰色关联度选择与该监测量关联密切的SCADA参数作为预测模型的输入量;使用正常状态下的SCADA数据训练LSTM预测模型,得出预测值和残差,通过3σ准则计算出上下预警阈值,用于风电齿轮箱状态监测和故障预警。某风电场风电齿轮箱的SCADA数据验证表明所提出的方法能有效预警风电齿轮箱故障。     

Effects of wind shear on wind turbine rotor loads and planetary bearing reliability

Recent studies suggest that wind shear and the resulting pitch moments increase bearing loads and thereby contribute to premature wind turbine gearbox failure. In this paper, we use momentum‐based modeling approaches to predict the pitch moments from wind shear. The non‐dimensionalized results, which have been validated against accepted aeroelastic results, can be used to determine thrust force, pitch moment and power of a general rotor as a function of the wind shear exponent. Even in extreme wind shear (m = 1), the actual thrust force and power for a typical turbine (R* < 0.5) were within 8% and 20% of the nominal values (those without wind shear), respectively. The mean pitch moment increased monotonically with turbine thrust, rotor radius and wind shear exponent. For extreme wind shear (m = 1) on a typical turbine (R* = 0.5), the mean pitch moment is ~25% the product of thrust force and rotor radius. Analysis of wind shear for a typical 750 kW turbine revealed that wind shear does not significantly affect bearing loads because it counteracts the effects of rotor weight. Furthermore, even though general pitch moments did significantly increase bearing loads, they were found to be unlikely to cause bearing fatigue. Analyses of more common low wind‐speed cases suggest that bearing under‐loading and wear are more likely to contribute to premature bearing failure than overloading and classical surface contact fatigue. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.The method is applied to Lillgrund and Horns Rev-I offshore wind farms and the results are compared with TI derived from the meteorological mast, nacelle mounted anemometer on the turbines and estimation based on the standard deviation of power. The results show that the proposed TI estimation method is in the best agreement with the meteorological mast. Therefore, the rotor effective wind speed is shown to be applicable for the TI assessment in real-time wind farm calculations under different operational conditions. Furthermore, the TI in the wake is seen to follow the same trend with the estimated wake deficit which enables to quantify the turbulence in terms of the wake loss locally inside the wind farm.