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闭环需求化风电场运维模式 总被引:1,自引:0,他引:1
摘要: 计划检修模式被中国大部分风力发电企业所采用。但这种检修模式存在诸如数据孤立、盲目维修、频繁维修及过度维修等问题。文章分析了以计划检修为主导的机组检修存在的优点和弊端,分析了国内外风电设备设备管理和维修的发展历史。针对计划检修模式存在的缺陷与不足,作者提出了基于需求的检修模式。该模式融合了PDCA循环管理、数理统计回归分析法、维修树等管理方法的优点。以一个49.5 MW风电场机组实时运行数据为应用实例,验证了该模式的可行性和优越性。 相似文献
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确保风电机组安全可靠运行具有重要的工程意义,文中提出对在役风电机组进行安全检测的技术体系,明确了采用层次分析法(AHP)对在役风电机组的安全评价方法.解决了各项安全检测技术的权重问题,并综合了专家的经验和意见,对风电机组的安全评价具有较好的研究意义. 相似文献
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确保风电机组安全可靠运行具有重要的工程意义,文中提出对在役风电机组进行安全检测的技术体系,明确了采用层次分析法(AHP)对在役风电机组的安全评价方法,解决了各项安全检测技术的权重问题,并综合了专家的经验和意见,对风电机组的安全评价具有较好的研究意义。 相似文献
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针对以往风电机组数字孪生建模受不同研究目的或单一软件的功能限制,难以建立风电机组整机模型的问题,提出一种新的风电机组孪生建模方法。该方法首先依托FAST风速性能模块,建立稳态风模型、随机湍流风模型以及风电场实时风速模型;接着采用空气动力学模块和结构动力学模块分别搭建风电机组叶片、塔架等关键部件的几何与动力学模型;最后在Simulink中搭建风电机组电气系统模型及控制策略,由此构建完整的风电机组孪生模型。将该孪生建模方法分别用于WindPACT 1.5 MW双馈风电机组与某风电场Fuhrl?nder 2.5 MW双馈风电机组并进行验证。结果表明:孪生模型在不同风速模型下,各重要生产参数相比设计标准及实际运行数据均具有较高的准确性。此外,通过对风电机组数字孪生系统实时仿真和现场不可测数据的孪生模拟,也进一步表明孪生模型具有可行性和有效性。 相似文献
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针对山地风电场大型永磁同步风电机组发电机定子温度的变化特点及传热过程进行分析,提出一种基于风电机组SCADA数据的发电机定子温度建模方法。首先,分析风电机组在实际运行过程中定子温度的变化情况;然后,根据永磁同步发电机内部的传热过程,对发电机定子温度建模;最后,采用风电机组正常运行时的SCADA数据求解模型参数。实例分析表明,风电机组状态正常时,定子温度估计的平均误差绝对值为0.59 ℃,模型精度较高;风电机组状态异常时,模型温度估计的平均误差绝对值为5.17 ℃,精度显著降低。 相似文献
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为规范风电机组地基基础设计工作,提高前期工作成果质量,WTF软件应用而生。软件包含扩展基础、桩基础、岩石锚杆基础和肋梁基础4个设计模块,满足风电地基基础设计规范要求的各种验算。结合WTF软件设计原理,分析说明软件主要升级功能点及参数选择方法。结果表明,软件建模方便,计算结果符合规范要求,能够有效节约风电地基基础设计人员的时间成本,固化计算过程,使其摆脱繁琐的公式计算,提高设计效率,促进风电地基基础设计工作的标准化、规范化。 相似文献
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从机组经济调度优化的角度入手,研究了风电消纳的合理应对方式。为使机组组合安排能在消纳更多风电的同时兼顾电力系统运行的安全、可靠性准则,并满足一定的经济性,分别以弃风电量、机组运行费用和机组运行风险度三方面为优化目标建立了机组组合的优化模型及这三者共同的多目标优化模型。利用粒子群算法及模糊多目标优化方法对模型进行求解,并将上述模型和算法应用于某10机算例的计算中。分析结果表明,该建模思路能为风电的有效接纳提供有益的指导。 相似文献
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为解决风电机组传动链易发生故障的问题,文章阐述了风电机组齿轮箱特征频率的计算方法和基于振动信号分析的故障特征提取方法。结合实际情况,以行星级齿轮磨损、中间轴小齿轮崩齿、高速轴齿轮崩齿和发电机轴承电腐蚀等典型故障为例,通过齿轮箱特征频率和传动链典型故障振动信号基本特征分析,可较好地完成故障识别。结果表明,采用经典信号处理方法能对上述典型故障进行特征提取,验证了经典方法对单一、明显故障特征提取的有效性,为深入开展传动链故障特征提取方法研究奠定了基础,为风电机组故障检修维护提供了技术支撑。 相似文献
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为了提高风能资源的有效利用,提高风电机组运行的可靠性、经济性和安全性,故障预测变得尤为重要。故障预测方法在判断设备隐患、制定合理的风电场运维方案方面具有重要的理论和实际意义。围绕变桨系统故障预测的问题,文章利用小波对机械信号特征敏感的优点,引入自适应阈值函数实现对小波降噪的改进,结合具有自学习能力和并行处理能力强的BP神经网络,建立了自适应阈值的小波BP神经网络故障预测模型。该模型结合了小波分析的技术特点,减少了噪声对预测模型的干扰,模型简洁、易实现。应用该网络预测模型,提前15 d对变桨系统故障预测的准确率达到了92.27%。 相似文献
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Jae Yoon David He Brandon Van Hecke Thomas J. Nostrand Junda Zhu Eric Bechhoefer 《风能》2016,19(9):1733-1747
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. 相似文献
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Renewable energy sources like wind energy are copiously available without any limitation. Reliability of wind turbine is critical to extract maximum amount of energy from the wind. The vibration signals in wind turbine's rotation parts are of universal non-Gasussian and nonstationarity and the fault samples are usually very limited. Aiming at these problems, this paper proposed a wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree Support Vector Machines (SVM). Firstly, the diagonal spectrum is calculated from vibration rotating machine as the input feature vector. Secondly, self-organizing feature map neural network is introduced to cluster the fault feature samples and construct a cluster binary tree. Then the multiple fault classifiers are designed to train and test samples. The wind turbine gear-box fault experiment results proved that this method can effectively extract features from nonstationary signals, and can obtain excellent results despite of less training samples. 相似文献
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风力机状态监测与故障诊断技术研究 总被引:6,自引:0,他引:6
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。 相似文献
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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. 相似文献