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。     

基于数据挖掘风力发电设备故障远程诊断研究

为保证维护风电场大型风电机组安全、可靠、经济和优化运行,本文提出了一个基于数据挖掘技术的风电机组在线状态监测与故障远程诊断系统。介绍了数据挖掘故障诊断系统的硬件、软件体系结构、网络技术及特点,举实例说明应用的实用性和有效性。     

风电机组振动监测系统对齿轮箱故障的检测与诊断

着重介绍了本特利内华达ADAPT.wind风电机组在线状态监测系统边带能量率的算法原理,并以在风电机组齿轮箱故障诊断中的应用实例,充分说明其对风电场运营业主提高风电机组管理水平和运营效率的有效性。     

基于EKF和UKF的风电机组非线性状态预测

风电机组的状态监测和故障诊断依赖于对其关键状态参数的参考值进行有效预测。文章采用基于系统模型的非线性状态评估方法对风电机组关键的状态参数进行预测。通过对风电机组进行建模仿真,构建扩展卡尔曼滤波和无迹卡尔曼滤波模型预测风电机组主轴转速和发电机电磁转矩,对比两种方法的预测结果在不同测量步长下的差异。结果显示,无迹卡尔曼滤波方法比扩展卡尔曼滤波方法具有更强的收敛性和稳定性,受测量步长影响更小。     

风电运营维护中的状态监测研究

通过状态监测与故障诊断技术的有效实施,实现基于可靠性的设计改造与运行维护,从而使风电机组提高运行效率,实现优质安全运行。     

某风电场风力发电机组故障诊断

为了准确判断风电机组的运行状态及故障,提出了基于常规分析—振动幅值分析—波形频谱分析的故障诊断流程,阐述了针对风电机组的幅值分析方法和波形频谱分析方法,并通过对某机组异响的根源探究实例,准确地诊断出机组异响来源于齿轮箱太阳轮,可为风电机组故障诊断技术提供依据。     

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。     

风电机组状态监测系统现状研究

实现风电机组状态监测是保证机组长期安全稳定运行的有效手段,文章论述了风电机组状态监测系统的构成和特点,对目前国内外现已开展的监测系统进行了综述,并对基于共振解调、最优滤波解调、无线通信的状态监测系统结合具体厂家作了详细分析。最后对状态监测系统的发展和存在的问题做了展望和总结,风电机组各部件监测机制的完善、专家系统的建立、报警阈值的设置以及研发有效可行的软硬件系统是目前风电机组状态监测需要解决的关键问题。     

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。     

风电机组状态检测技术研究现状及发展趋势

状态检测技术是风电机组故障诊断与运营维护最为重要的技术手段。对风电机组进行状态检测能够掌握机组的健康状态及发电性能,以便及时制定维护维修策略和采取提升发电性能的技改措施、减少机组停机时间、避免重大故障发生、节省维修成本、提高机组发电能力。因此,在风电机组状态评价和维护维修中,针对状态检测技术进行了大量的研究和应用。文章从风电机组状态检测特点、机组类型和故障特点3方面进行归纳总结;从风电机组健康状态检测和性能状态检测两方面,综述了近年风电机组状态检测的研究现状和重要的研究成果;探讨了目前风电机组状态检测面临的问题,从状态检测设备和软件集成化、状态检测智能化和标准化等方面解决所面临的问题。文章指出,故障机理分析、多状态检测融合技术和统一平台的综合健康检测评估系统是风电机组状态检测发展的新趋势。     

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。     

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。     

基于BP网络的水轮机调节系统状态监测与故障诊断

提出了一种基于参数的水轮机调节系统状态监测和故障诊断方法,并设计了基于BP人工神经网络的实现模型,对该方法中用到的数据处理技术做了深入研究,为水轮机调节系统的状态监测和故障诊断研究提供了一种新思路。     

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.     

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.     

Convolutional neural network framework for wind turbine electromechanical fault detection

Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high-dimensional raw condition monitoring data for the automatic detection of multiple wind turbine electromechanical faults. The proposed approach leverages the potential of combining information from a variety of signals to learn features and to discriminate the types of fault and their severity. As a result of the CNN layers used to extract features from the signals, this architecture works in the time domain and can digest high-resolution multi-sensor data streams in real-time. To overcome the inherent black-box nature of AI models, this research proposes two interpretability techniques, multidimensional scaling and layer-wise relevance propagation, to analyse the proposed model's inner-working and identify the signal features relevant for fault classification. Experimental results show high performance and classification accuracies above 99.9% for all fault cases tested, demonstrating the efficacy of the proposed fault-detection system.     

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.     

大型风力发电机组变桨电池充电故障分析处理

风力发电机组的控制系统多采用工业大规模集成系统进行运算控制,提高了抗干扰能力。控制系统通过光纤与监控室的计算机连接,可以远距离对风机进行实时监控,大大降低了运行人员的工作量和劳动强度,是进行远程运行数据统计分析及故障原因分析的重要手段。当风机出现报警停机时,如何准确地判断故障类型和原因并给出故障处理的方法对于风机的安全运行至关重要,文章总结分析了风力发电机组变桨电池充电故障的现象、原因、分析过程以及处理方法,保证了风机的正常运行,为同类故障提供了参考。     

一种基于主成分分析与支持向量机的风电齿轮箱故障诊断方法

为解决风电齿轮箱状态监测数据样本量较少,特征指标间存在相互干扰且具有非线性难以分类等问题,本文提出了一种基于主成分分析结合支持向量机的风电齿轮箱故障诊断方法。首先,采用主成分分析法(PCA)对原始数据进行降维,做出第1,2主成分二维图及前3个主成分三维图,表明PCA对监测状态数据具有一定的分类效果。其次,提取累计贡献率80%以上的前5个主成分作为数据集。最后,采用支持向量机(SVM)比较4种不同核函数的诊断准确度,并加入噪声验证。分析结果表明：径向基核函数构建的支持向量机总体分类精度达到97%,准确率最高;在含噪的情况下,线性核函数与径向基核函数分类精度达到94%;与MLP神经网络进行对比发现,支持向量机更适应小样本分析且测试精度较高。实例分析表明,主成分分析结合支持向量机有较好的分类效果,适用于风电齿轮箱故障诊断的工程应用。