基于非线性状态估计的风电机组变桨控制系统故障识别

发布时间：2018-08-13 17:36

【摘要】：变桨系统是风力发电机组中控制算法复杂,设备故障率较高的子系统。当风机出现故障停机时,数据采集与监视控制(supervisory control and data acquisition,SCADA)系统在故障出现会显示出具体的故障信息,该具体故障信息往往包含多个,给风机迅速定位故障类别、确认检修方式、及时有效地恢复生产造成了不利影响。因此,基于变桨运行数据的风力发电机组主故障识别对于提升机组可利用率及机组发电性能有着积极重要的意义。文中采用非线性状态估计技术作为数据挖掘方法,在某风电场机组SCADA数据基础上,分析机组变桨系统运行趋势及故障类型建立机组变桨控制系统主故障模型,并对该模型进行验证。研究结果表明,基于非线性状态估计的风电机组变桨控制系统主故障识别能够在多个故障信息中识别出主故障,次故障等,从而指导风场检修人员确定检修顺序,并为后续开展风机性能分析及评价提供了新的思路。
[Abstract]:Variable propeller system is a subsystem of wind turbine with complex control algorithm and high failure rate. When the fan fails to stop, the data collection and monitoring control (supervisory control and data acquisition system will display the specific fault information when the fault occurs. The specific fault information often contains more than one fault information, which can quickly locate the fan's fault category and confirm the maintenance mode. The timely and effective resumption of production has had a negative impact. Therefore, the identification of main faults of wind turbine based on variable propeller operation data is of great significance to improve the utilization ratio and generation performance of wind turbine. In this paper, the nonlinear state estimation technique is used as a data mining method. Based on the SCADA data of a wind farm unit, the main fault model of the variable propeller control system is established by analyzing the running trend and fault types of the turbine variable propeller system. The model is verified. The research results show that the main fault identification of wind turbine variable propeller control system based on nonlinear state estimation can identify the main fault and secondary fault in multiple fault information, thus instructing the maintenance personnel in wind field to determine the repair order. It also provides a new idea for the following analysis and evaluation of fan performance.
【作者单位】：中能电力科技开发有限公司;
【分类号】：TM315

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