摘要
为减少风机塔筒倾斜和倒塔事故,增强早期预警及在线监测,本文采用实验法、直接测量、对比分析等方法,对一种基于KxM人工智能系统建立的风机塔筒倾覆监测系统的实际应用开展研究。以基础不均匀沉降值、基础松动值、塔筒垂直度、塔筒结构松动值为检测标准,首先采用实验法对监测系统的理论准确性进行验证,其次利用沉降仪直接测量实际环境数据,与监测系统在线监测数据进行对比,分析了监测系统的实际运用效果,同时在以单故障条件检测该人工智能系统数据准确性的基础上,对系统进行多故障对比检测分析,通过实测值和特征值的比较分析,认为该监测系统在多故障条件下监测效果基本与实际相符。该监测系统的实际运用可为快速区分故障类别、快速处理故障提供一定的依据,以减少风机事故及故障的发生率。
In order to reduce the fan tower tilting and tower toppling accidents, strengthen early warning and on-line monitoring, this paper adopts the methods of experiment, direct measurement and comparative analysis, etc. The practical application of an overturning monitoring system for fan tower based on KxM artificial intelligence system is studied. Firstly, the theoretical accuracy of the monitoring system is verified by the experimental method with the uneven settlement value of the foundation, the looseness value of the foundation, the perpendicularity of the tower tube and the looseness value of the tower tube structure as the testing standards. Secondly, the actual environmental data are measured directly by the sedimentation instrument, and the results are compared with the on-line monitoring data of the monitoring system. At the same time, on the basis of checking the data accuracy of the artificial intelligence system under the condition of single fault, the system is detected and analyzed by comparing the measured value with the characteristic value. It is considered that the monitoring effect of the system is basically consistent with the practice under the condition of multi-fault. The practical application of the monitoring system can provide a certain basis for the rapid classification of faults and the rapid treatment of faults, so as to reduce the occurrence rate of fan accidents and faults.
作者
李文明
张云
LI Wenming;ZHANG Yun(Wuling Power Co.,Ltd.,Changsha 410000,Hunan,China)
出处
《电力大数据》
2022年第1期9-17,共9页
Power Systems and Big Data
关键词
监测
风电机组
塔筒倾覆
人工智能
实测值
特征值
monitoring
wind turbine
tower overturning
artificial intelligence
measured value
characteristic value
