风力发电机状态监测与故障诊断系统研究

风力发电是电力生产的新型途径,对于提高电力生产量、节能降耗、电力行业的可持续发展具有积极影响.但是风力发电机装机容量和建设规模不断扩大,操作要求较高,如果维护措施不到位,将导致故障的频发.对此,需采用在线诊断系统实现设备运行过程的实时监控,及时发现和解决问题.本文首先对风力发电机组结构进行分析,其次就风力发电机状态监测与故障诊断系统设计进行阐述,然后对故障诊断方法进行总结.     

SOM神经网络在风力发电机组故障诊断中的应用

风力发电正在电力行业中占有越来越重要的位置。然而因为所处的环境条件恶劣,风力发电机经常容易发生故障。传统的状态监测与故障诊断方法较为费时费力,又因为无法采集到所有的故障信息,所以BP神经网络无法做出正确诊断。因此,将SOM神经网络应用于风力发电机组的振动故障诊断中。用正常运行的样本数据对网络进行训练,根据检测样本输出神经元在输出层的位置对是否发生故障进行判断。经实例分析证明,该方法可对风电机组的故障进行有效诊断。     

关于风电场风电机组中风力发电机的运行维护分析

本文通过风电机组中风力发电机的常见故障分析,针对故障诊断难点问题,优化风力发电机的运行维护环节,重点加强故障难点的维护工作,为风电场风电机组的运行维护提供新的思路。     

风力发电机组中齿轮箱的故障诊断分析

齿轮箱运行的稳定对于保证风力发电系统的正常运行有着重要的作用，要想保证齿轮箱运行的稳定，对其故障的诊断至关重要。只有通过合理的故障诊断，才能够判断出齿轮箱的故障部位、故障程度，才能够采取针对性的预防措施和维修措施，从而有效降低故障损失。本文从风力发电机组齿轮箱故障诊断的必要性入手，研究了风力发电机组齿轮箱故障的机理以及相关诊断方法和技术的选择，最后分析了几种主要的齿轮箱故障诊断技术，旨在为风力发电机组齿轮箱故障诊断的相关研究提供参考。     

基于BP神经网络和D-S证据理论的发电机故障综合诊断

本文以发电机故障为研究对象,提出了一种基于BP神经网络和D-S证据理论相结合的信息融合的故障诊断方法,并进行了验证。利用BP神经网络对测量数据进行局部分析诊断,最后利用D-S理论对局部诊断结果进行融合,得到的结果基本满足需求,从而证明了BP神经网络和D-S理论相结合的综合诊断方法的可行性和实效性。     

MW级风力发电机故障诊断系统

研究了风力发电机组的故障诊断技术,以西门子安全系统解决方案作为硬件基础构建故障诊断系统,利用故障树分析法和专家系统对风力发电机组进行故障诊断,运用ST语言,开发了基于SCOUT软件的风力发电机组状态监测故障诊断系统。文章还介绍了风力发电机安全链系统的总体结构、特点和主要实现方法,对以西门子分布式安全系统为基础的安全链进行了分析。     

智能烟草工厂底层物联设备远程检修系统设计

针对烟草工厂底层物联设备检修体系落后,设备检修效率低的问题,设计智能烟草工厂底层物联设备远程检修系统.采集现场设备运行数据,并将采集的数据存储至设备故障经验知识库,应用故障专家子系统调用设备故障经验知识库的数据信息,实时监控现场设备运行状态;利用故障树最小割集的故障诊断方法,诊断智能烟草工厂底层物联现场设备,依据诊断结...     

对于风电场电气设备中风力发电机的运行维护的措施

风力发电机是实现风能、机械能与电能之间相互转化的机电设备,在风电场中风力发电机是十分重要的设备,长期使用过程中风力发电机很容易出现各种故障问题,对此必须要加强对各种故障的处理,提高风力发电机的运行效率.本文对风力发电机运行维护的相关策略进行分析和探讨.     

小波神经网络对风力发电机组功率变流器的故障诊断

研究了永磁同步风力发电机组网侧功率变流器开关器件的开路故障问题,提出一种新的开路故障诊断方法,以实现对单个或两个开关器件同时开路的多种故障进行诊断。首先采集不同故障下网侧功率变流器的电压信号作为原始信号,利用小波变换对此信号进行分解和重构,再对小波变换后得到的数据进行特征分析和判定,将特征分析和判定后的数据作为BP神经网络的输入进行决策分类。仿真结果表明,该方法能够区分网侧功率变流器多开路故障,且诊断准确率高。     

基于MCU的嵌入式风机在线监测系统研究

本文介绍了一种基于多台MCU和一台PC机构成的分布式风力发电机在线监测系统,采用嵌入式数据采集方式,通过对风力发电机组的转速、振动频率、温度、湿度等参数进行实时监测,将模拟信号转化为数字信号后传送给PC机,以便保证安全运行和在线故障监测,从而确保对风力发电机组的运行情况进行实时掌控。     

基于多信号流图的飞控系统实时故障诊断

针对飞控系统构造复杂、解析建模困难的特点,给出一种基于多信号流图模型进行实时故障诊断的方法。该方法首先建立系统的多信号流图模型,然后根据模型得出相关矩阵,在相关矩阵和各实时测试结果基础上按照实时故障诊断基本算法进行诊断推理,判定系统各模块所处的状态。对某型飞机飞控系统的建模以及模拟诊断结果表明,该方法能隔离多故障,同时降低虚警率,是一种十分有效的实时故障诊断方法。     

基于二分图模型的通信信息网络故障联合定位

国家电网信息通信网络依靠两套运维系统,分别实现对信息网络与通信网络的故障定位与分析,然而通信网络故障往往会引发信息网络故障,如何高效精确地进行通信信息网络故障联合定位是亟需解决的问题.针对信息通信网络的联合故障定位问题,提出了基于二分图模型的故障联合定位算法.首先依据通信网网络节点的关联性对网络分簇,并将每一簇作为一个子域.其次在每个子域内建立基于二分图的故障关联影响模型,最终利用目标排序法并行地对多个子域内网络故障进行分析,从而实现通信信息网络关联故障高效精确的联合定位.实验结果表明,该联合故障定位分析方法的故障诊断率达85％～95％.     

A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge

Condition monitoring of turbine generators, housed at British Energy nuclear power stations throughout the U.K., is implemented to diagnose incipient faults at an early stage, so corrective action can be taken to avoid the associated high costs of an unplanned shutdown. A prototype expert system has been developed that provides decision support to condition monitoring experts who monitor British Energy turbine generators. The expert system automatically interprets data from strategically positioned sensors and transducers on the turbine generator by applying expert knowledge in the form of heuristic rules. This paper reviews the application domain and describes the work undertaken in developing the prototype expert system. The paper also outlines a learning module design that uses an approach based on an analytical symbolic machine learning technique, explanation-based generalization, to semiautomatically derive heuristic rules for turbine generator fault diagnosis. The approach adopted by the learning module is explained in detail and a worked example demonstrates how the learning module can derive a fault heuristic from a single training example. The modular approach to capturing the causal fault and behavioral models is described, and the method in which the module will be integrated with the existing expert system has been outlined. A preliminary evaluation of the learning module design is discussed.     

Graph Representation and Diagnosis for Multiunit Faults

A method using graph theory is proposed for the detection and the location of a multiunit fault in a system. The method requires only a slight increase in the number of internal monitoring terminals over the number required for the 1-unit fault diagnosis. A graph representation of a system initially leads to a rectangular diagnostic matrix. An algorithm is developed for constructing a square reachability matrix from the diagnostic matrix. A graph derived from the reachability matrix permits diagnosis of multiunit faults.     

Testing of Synchronizers in Asynchronous FIFO

This paper presents a test method for testing two-D-flip-flop synchronizers in an asynchronous first-in-first-out (FIFO) interface. A faulty synchronizer can have different fault behaviors depending on the input application time, the fault location, the fault mechanism, and the applied clock frequency. The proposed test method can apply the input patterns at different time and generate capture clock signals with different frequency regardless of phase-locked loop (PLL) of the design. To implement the proposed test method, channel delay compensator, delayed scan enable signal generator, launch clock generator, and capture clock generator are designed. In addition, a well-designed calibration method is proposed to calibrate all programmable delay elements used in the test circuits. The proposed test method evolves to several test sections to detect all possible faults of the two-D-flip-flop synchronizers in the asynchronous FIFO interface.     

Synergistic use of soft computing technologies for fault detection in gas turbine engines

In this paper, we present a synergistic approach to startup fault detection and diagnosis (FDD) in gas turbine engines. The method employs statistics, signal processing, and soft computing techniques in a complementary manner to address fault detection at transient conditions. Traditional turbine engine FDD methods are based on engine data collected at steady-state conditions. However, incipient faults are difficult to diagnose using steady-state engine data; only engine faults that are fairly developed can be detected using conventional methods. Because incipient engine component faults are often manifest in the engine startup characteristics, we present a method to characterize the engine transient startup. Engine sensor data during engine startup are recorded in time series format. The sensor profiles corresponding to "good" and "bad" engine startups are sampled using the bootstrap technique. A feature vector is extracted in two steps, and signal processing is followed by the feature vector selection. In the signal processing step, principal component analysis (PCA) is applied to reduce the samples consisting of sensor profiles into a smaller set. In the feature vector selection step, a cost function is defined, and important discriminating features for fault diagnosis are distilled from the PCA output vector. The features obtained from this step are then classified using neural-network-based methods. The "leave-one-out" approach to cross validation is applied to obtain an objective evaluation of the neural network training. The proposed FDD method is evaluated using actual engine startup data, and the results are presented.     

基于LabVIEW与BP神经网络测距终端故障诊断系统研究

结合LabVIEW图形化编程特点和Matlab强大的矩阵运算能力,利用二者混合编程实现BP网络算法改进,设计了基于LabVIEW与BP神经网络的测距终端故障诊断系统,且通过对测距中参考信号发生器单元电路产生的硬故障进行识别诊断,以检验其可靠性。应用结果表明,该系统不仅能对多种未知故障进行准确分类识别,并可将结果通过简单直观的界面显示出来,从而降低故障诊断人员的工作难度,具有较强的实用价值。     

SVM技术与ANN方法对旋转机械故障诊断性能的比较

论述了基于支持向量机故障诊断技术的基本原理;介绍了传统的基于人工神经网络的故障诊断方法;以旋转机械故障诊断为例对两种诊断方法进行了比较,实验结果表明,与神经网络相比,基于支持向量机的故障诊断方法在训练速度、诊断精度、可靠性等方面都表现出了优越的诊断性能。     

基于电压暂降测量的配电网故障自动定位

文中提出了一种结合电力系统分析程序、数据库检索方法和模式识别技术自动识别的11kV配电网故障定位方法。该方法采用三相不平衡潮流和故障分析程序,对配电网各节点的故障进行分析生成电压暂降数据库,设计了数据库搜索和模式识别算法,以便配电网在发生实际故障时识别故障位置。利用电力系统EMTDC模拟器对该方法进行了评估和测试。实验结果表明,提出的线性模式识别算法能够识别所研究的11kV配电网的大部分故障区段。     

多属性融合的电力设备红外热特征数字化方法

本文针对电力设备红外图像诊断中热故障特征提取和数字化表达难题,提出一种多属性融合的电力设备红外热特征数字化方法。通过对电力设备热故障特性和相关诊断文件研究分析,在对图像预处理的基础上,提取图像中关键发热区域的热点温度、热点温差、发热面积、位置信息以及热点群聚现象等热属性值,构建多属性信息融合的过热性故障特征值向量,实现热故障特征数字化描述。以断路器为例对该方法进行了验证分析,结果表明,该方法对典型红外故障图谱具有良好的描述能力,可用于后续大量复杂故障样本情况下的设备热故障智能分类与诊断应用中。