基于现有风电远控系统采集数据的大数据分析及应用

发布时间：2018-03-19 03:08

本文选题：风电机组　切入点：大数据　出处：《华北电力大学(北京)》2017年硕士论文　论文类型：学位论文

【摘要】：随着能源结构的转型和调整,风力发电迅速发展,成为主要发电方式之一。风电场通常位于偏远地区,运行条件相对较差,国家监测和对机组的安全和经济性能评估至关重要。今天,大数据正在推动新一轮技术创新,这项关键技术已经发展到各行各业,风电行业也不例外。在本文中,首先通过对大规模风场故障数据的统计分析,形成了风力发电设备主要部件的故障模式及其分布;在此基础上,研究了双馈式风力发电机组(DFIG)的故障模式、机理、原因和诊断方法,应用故障树分析给出风电就主要部件的故障模式,为风电设备的运行维护提供了技术支持;最后,分析了双馈式风电机组主要部件齿轮和轴承的振动信号特点,以及故障齿轮和轴承的特性信息提取方法。本文的研究以国内风电公司为对象,系统收集和分析风电场设备的故障数据,获得风力发电机组的主要数据、组件故障率和停机时间数据的分布,形成组件级故障模式。为进行高效的风力发电机组运行和维护管理提供参考数据和技术支持。同时针对国内风电场常见的部件超温报警故障进行了分析。由于风力发电系统较为复杂,造成过热故障的原因很多,运行过程中的故障模式和影响不尽相同,因此深入研究的超温故障模式及其对机组整体影响的分析对提高风机设备的可用性和经济性具有重要的工程实践意义。
[Abstract]:With the transformation and adjustment of the energy structure, wind power has developed rapidly and become one of the main modes of power generation. Wind farms are usually located in remote areas and operate under relatively poor conditions. State monitoring and evaluation of unit safety and economic performance are essential. Today, big data is promoting a new round of technological innovation, a key technology that has developed into various industries, as well as wind power. Firstly, the fault mode and distribution of the main components of wind power equipment are formed through the statistical analysis of large-scale wind field fault data, and the fault mode and mechanism of DFIGR are studied. The reason and diagnosis method, using fault tree analysis to give the fault mode of main components of wind power, provide technical support for the operation and maintenance of wind power equipment. Finally, The characteristics of vibration signals of gears and bearings, the main components of doubly-fed wind turbines, and the methods of extracting characteristic information of fault gears and bearings are analyzed. The system collects and analyzes the fault data of wind farm equipment, obtains the main data of wind turbine, the distribution of component failure rate and downtime data, To provide reference data and technical support for the efficient operation and maintenance management of wind turbines. At the same time, the common parts overtemperature alarm faults in domestic wind farms are analyzed. The power generation system is more complex. There are many reasons for the overheating failure, and the mode and influence of the failure during operation are different. Therefore, the analysis of the overtemperature fault mode and its influence on the whole unit is of great significance in engineering practice to improve the availability and economy of fan equipment.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM614

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