碳纳米管在高电压设备在线监测领域的应用

阐述了高压电气设备绝缘故障在线检测的一些方法,介绍了碳纳米管气体传感器近期的研究动向和取得的成果。应用碳纳米管气体传感器监测电气设备绝缘故障特征气体时,重点介绍了纯SF6,SF6/N2混合气体中局部放电产生的气体分解组分在电压、气体压强、温度等因素下对气体传感器的影响。并提出了碳纳米管气体传感器在高电压设备在线检测领域研究中所存在的问题和未来的研究发展方向。     

绝缘子保护的FBG温度传感器研究

电力系统的输变电设备长期暴露在自然条件下,电气设备绝缘表面容易劣化,导致输电线路的故障。研制了具有绝缘子保护的光纤Bragg光栅(FBG)温度传感器用于检测电气设备常见发热部分,光纤外表面包覆单片或者多片硅橡胶材料的光纤绝缘子,整个传感器采用室温硫化硅橡胶材料一体成型结构加工而成。绝缘子保护光纤,使得传感器可以实现电气设备高电压下的检测,传感器测温时,测点温度变化会引起FBG反射波长的改变。对具有绝缘子保护的FBG温度传感器进行性能测试,实验采用恒温槽加热并用国家二级精度的水银温度计作为测量标准,对传感器进行20次从20～90℃的升温试验,温度每升高10℃记录一次波长。试验结果表明:传感器的灵敏度系数为10.2 pm/℃,线性度为0.70%FS。     

变电站高压电气设备绝缘在线监测技术探析

高压电气设备绝缘监测可提高电力系统运行的安全性。随着电网容量的增加,高压电气设备绝缘监测面临着新的挑战。通过详细介绍高压电气设备绝缘在线监测技术,探讨了变电站高压电气设备绝缘在线监测系统的应用问题,以供相关单位参考。     

浅谈高压电气绝缘试验中常见的问题

目前我国的生产以及各个行业,都离不开一些电气设备。而在这些电力系统中,设备与设备之间、设备与接地之间的绝缘相当重要。一旦发生绝缘失效情况,那么就极有可能导致高压电气设备出现放电并击穿,从而出现严重的电力系统故障。为了考核电气设备是否具有安全运行的基本条件,就需要对相关设备进行对应的试验并记录一些试验数据。通过对试验结果的比较,提高设备安全性。本文针对高压电气试验中存在的问题做简要的分析。     

高压设备放电紫外图像光斑区域特征提取

针对传统的高压电气设备紫外漏电检测方法存在的紫外图像处理算法复杂、精确度低等问题,采用基于高斯函数色彩映射的图像分割算法分割进行图像预处理。通过绝缘子污秽放电实验,拍摄高压故障设备紫外辐射图像,对紫外图像进行图像分割处理。从紫外光斑的二值图像中提取故障设备紫外辐射光斑区域特征,评估高压绝缘设备故障等级。实验过程中,在不...     

高压动力电池组绝缘性能的实时监测研究

传统的绝缘电阻监测方法不具备实时检测的功能,特定条件下无法监测,且抗干扰能力差。具有一定的局限性。本文在分析传统绝缘电阻监测方法的基础上,提出一种注入低频交流信号的有源式绝缘电阻监测方法,基本实现了绝缘电阻的故障实时监测.该检测方法分为两个阶段：故障检测、绝缘电阻计算。其中,故障检测：通过测量“测量电阻”两端电压,计算出正、负绝缘电阻的并联电阻值,并由此判断绝缘电阻故障状态;检测出绝缘电阻故障后,进行绝缘电阻的精确测量：断开负载、交流信号源,分别与正负极绝缘电阻并联接入两个电阻并测量其两端电压,计算出正负极绝缘电阻值。仿真结果表明,故障误报率小于2.15%,基本实现了绝缘电阻的故障实时监测。这项技术能够有效保证微电网蓄电池储能电站的绝缘性能,一旦事故发生,能够及时的发现故障,排除隐患,继续安全、稳定的运行。因此对高压电池组在实际生产中有较大的应用价值和意义。     

基于物联网技术的高压电气结点温度监测系统研究

针对基于物联网技术的高压结点测温系统进行研究,填补了国内有关技术的空白,为高压设备结点温度的实时监测、自动报警提供了一种先进的、行之有效的技术手段.基于物联网技术的高压结点测温系统的推广使用,可以为各种高压电气设备在不同环境下的正常使用提供技术依据,从而为巡线检修、设备保养和更换提供科学依据,进而预防发生因温度过高而引起的设备爆炸和损坏.高压电气设备结点发热与线路负荷大小、结点安装工艺、工作环境等有关.实时了解电气结点温度变化,及时进行负荷转移、改善安装工艺等,减少发生故障的几率,提供供电可靠性.     

多参数电气设备绝缘微机智能化在线监测

分析了获取不同类型电气设备绝缘信号的特点,研制了相应的传感器和多参数微机绝缘智能化在线监测系统,通过实际运行表明,传感器性能稳定,监测系统数据采集和测量可靠。     

基于知识图谱的电力变压器绝缘故障监测方法

通过传感器采集电力变压器数据，只能获取当前运行数据，导致变压器绝缘故障监测结果与实际情况不一致，为此提出基于知识图谱的电力变压器绝缘故障监测方法。采用RBF神经网络建立了变压器绝缘故障监测模型；依据知识图谱三元组成和生命周期，设计数据抽取步骤，抽取变压器历史数据，构建电力变压器知识图谱；设置变压器温度和功率值，设计故障监测步骤，实现变压器绝缘故障监测。实验结果表明：本文方法在案例1和案例2中，监测变压器绝缘故障产生原因与案例设置原因一致，监测效果较好。     

基于ZigBee技术高压开关柜温度在线监测系统研究

为了解决高压开关柜触头温度在线监测的实时通信、远程测控、低功耗和低成本等问题,以荧光式光纤测温传感器为基础,结合ZigBee短距离无线通信及RS485总线网络,设计一种高压开关柜温度在线监测系统。通过温度传感器分节点、ZigBee协调器节点和远程监控主站的软硬件设计,实现了温度数据的实时采集、无线传输和集中控制等功能。实测数据对比分析表明,在线监测系统实测数据的误差和温升变化率均满足高压电气设备温度监测应用需求,系统集成封装功能完备,应用效果好。     

High-voltage equipment condition monitoring and diagnosis system based on information fusion

As high-voltage electric equipment has complex structure and works in harsh environments, fiber Bragg grating (FBG) sensors are applied to realize the real-time monitoring of some parameters in which temperature is the main parameter. Using FBG sensors to monitor temperature of high-voltage electric equipment can overcome the disadvantages of harsh monitoring environment such as high-voltage, big current, strong electromagnetic interference and so on. The fault of high-voltage electric equipment is difficult to be distinguished as there may be many different reasons. The traditional or simple methods cannot totally meet the demand of fault diagnosis of high-voltage electric equipment. First, taking neural network as a classifier to distinguish different fault types from complex fault information in the feature layer can supply a good foundation to final information fusion diagnosis. Second, Dempster–Shafer evidence theory is used to make a comprehensive diagnosis of fault information in the decision layer. All the uses above can increase the speed and accuracy of diagnosis and have practical significance. The fault diagnosis system shows good results and provides an effective way to realize the real-time condition monitoring and more accurate fault diagnosis of high-voltage electric equipment.     

基于粗糙集与神经网络的断路器状态监测

断路器的故障诊断对于事故后快速恢复具有重要意义,然而全面、准确的故障诊断仍是个难题。本文将电寿命和机械状态综合考虑,从而实现了对断路器整体健康状态的评估。其中机械状态的检测,提出了基于神经网络(Neural Network)并结合粗糙集理论(Rough Set Theory)的方法。实验数据表明,该方法提高了诊断的全面性、准确性和预测精度。     

大型机电设备电气参数监测与故障诊断装置

提出采用实时在线监测电气参数方法,并结合物联网、WIFI无线通信、VC编程与数据库管理等多种技术,研制出物联大型机电设备电气参数实时检测与故障动态诊断装置,以实现机电设备电气参数的实时在线监测、故障动态诊断、预警以及设备与故障诊断中心间的双向无线操作。实践表明,本装置能有效避免故障发生,减少故障停机损失,并为"预知维修"提供技术保障。     

矿用高压配电装置隔离触头温度在线监测系统的设计

针对煤矿井下高压配电装置隔离触头常因接触不良引发温度过高,继而导致故障且不易检测的问题,提出了一种矿用高压配电装置隔离触头温度在线监测系统的设计方案,重点阐述了该系统下位机软件程序和基于LabVIEW虚拟仪器开发平台的上位机在线监测程序的设计。试验结果表明,该系统能够实时远程监测矿用高压配电装置隔离触头的温度信息,误差小于1℃,符合工程实际要求,且能够根据监测的温度信号进行故障诊断和预警。     

一种高压开关综合数据采集与监测系统设计

高压开关是电力系统中实现电路分合控制的重要电力装备,对高压开关进行在线监测可以实时监测其运行状态,进而为故障诊断、预测、电寿命、绝缘等分析提供数据来源,可以提高高压开关整体运行可靠性和减少故障时间;数据采集与监测装置是实现这种在线监测功能的核心装置,基于FPGA+STM32双处理器架构的高速数据采集系统,利用FPGA灵活的可编程特性实现高速并行数据采集,利用STM32微控制器强大的处理能力和丰富的外围接口实现数据处理、控制和传输,FPGA和STM32之间通过FSMC接口实现快速数据交换;装置采集了高压开关设备多个传感器数据,通过工业串口液晶屏作为本地人机交互界面,同时以太网接口通过TCP协议上传数据给远端,实现了高压开关运行状态的在线监测。     

高压设备无线温度监控系统的设计

针对高压电力设备周围强电磁场的工作环境,设计一套温度监控系统。系统采用ZigBee无线通信技术,实现了高压隔离。系统中测温节点采用电池供电,为保证高压设备连续运行,测温节点采用低功耗设计,使得2节电池续航时间达到2年以上。测温节点获取的数据通过路由节点最终汇集到一个协调器节点,再通过RS-485总线发送给监控中心,监控端计算机对数据存储并进行梯度计算等处理分析,从而实现监控和预警等功能。该系统也可辅助智能电网对设备进行监控,保障电网的安全运行。     

Ecological interface design and computer network management: The effects of network size and fault frequency

This article describes an experiment investigating the impact of ecological interface design (EID) on human performance in computer network management. This work domain is more dynamic than those previously studied under EID because there is a constant potential for the addition and removal of devices, as well as changing configurations, making it important to study the generalizability of the framework. Two interfaces were created for the University of Toronto campus network consisting of 220 nodes: a P interface based on existing design practices which presented primarily physical information and a P+F interface based on EID which presented both physical and functional information identified by an abstraction hierarchy analysis. Participants used one of the two interfaces to detect and diagnose faults or disturbances in the simulated network in real-time. Network size and fault frequency were both manipulated as within-participants variables. The P+F interface led to faster detection times overall, as well as improved fault detection rate and more accurate fault diagnosis under higher fault loads. These results suggest that the EID framework may lead to more robust monitoring performance in computer network management compared to existing interfaces.     

Intelligent fault diagnosis in microprocessor systems for vibration analysis in roller bearings in whirlpool turbine generators real time processor applications

Large steam turbines used for electrical power generation demand governing systems of very high integrity (safety) and availability. The latest generation of electronic governors uses microprocessors in a distributed, two level architecture to achieve the required integrity and availability and in addition provides greater configuration flexibilities and wider facilities than earlier governors. Rolling element bearings are one of the major machinery components used in industries like power plants, chemical plants and automotive industries that require precise and efficient performance. Vibration monitoring and analysis is useful tool in the field of predictive maintenance in small hydro electric power plants. Health of rolling element bearings can be easily identified using vibration monitoring because vibration signature reveals important information about the fault development within them. Numbers of vibration analysis techniques are being used to diagnosis of rolling element bearings faults. This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Initially, the proposed work explores the Continuous Wavelet Transform (CWT) to adaptively remove the exact noises from vibration analysis and then feature extraction is performed by exploiting the noise removed pre-processed data. Statistic filter (SF) and Hilbert transform (HT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and Special bearing diagnostic symptom parameters (SSPs) in a frequency domain that are sensitive to bearing fault diagnosis are defined to recognize fault types. The SF is first used to adaptively cancel noises, and then fault detection is performed by exploiting the optimum symptom parameters in a time domain to identify a normal or fault state. For precise diagnosis, the SSPs are calculated after the signals are processed by M-PH and HT.     

A method for condition monitoring and fault diagnosis in electromechanical system

Condition monitoring of electrical machines has received considerable attention in recent years. Many monitoring techniques have been proposed for electrical machine fault detection and localization. In this paper, the feasibility of using a nonlinear feature extraction method noted as Kernel independent component analysis (KICA) is studied and it is applied in self-organizing map to classify the faults of induction motor. In nonlinear feature extraction, we employed independent component analysis (ICA) procedure and adopted the kernel trick to nonlinearly map the Gaussian chirplet distributions into a feature space. First, the adaptive Gaussian chirplet distributions are mapped into an implicit feature space by the kernel trick, and then ICA is performed to extract nonlinear independent components of the Gaussian chirplet distributions. A thorough laboratory study shows that the diagnostic methods provide accurate diagnosis, high sensitivity with respect to faults, and good diagnostic resolution.