摘要
随着电站规模扩大,传统人工巡检与工业监控相结合的电站巡检模式往往存在无法自动识别判断故障及信息反馈敏感度低等问题。结合5G技术和人工智能,引入变分模态分解(VMD)及图像灰度处理技术对电站内机组运行数据进行分析,结果表明:图像分形维数存在30Hz与85Hz的频率特征,幅值对应分别为0.02和0.009,为主频和次频,且远远强于其他杂频。VMD方法成功地分解了各监测点的压力脉动信号,获得了时域和频域上的各模态函数特征。通过分析尾水管处两个监测点的VMD分解结果发现,其频率成分相似且与蜗壳内部监测点的频率一致。本文的研究结果可以为电站的智能化建设提供重要支持,同时为电站运行和维护提供更有效的手段。
As the scale of hydropower stations expands,traditional manual inspection combined with industrial monitoring often faces more problems such as inability to automatically identify and judge faults,and low sensitivity to information feedback.This paper describes a new method for applying variational mode decomposition and image grayscale processing techniques to an analysis of the operating data of hydropower plant units,through combining 5G technology and artificial intelligence.The results show that the fractal dimension of the images features two typical frequencies of 30 Hz and 85 Hz,with the corresponding amplitudes of 0.02 and 0.009 respectively,which are detected as the dominant and secondary frequencies,far stronger than other clutter frequencies.The VMD method successfully decomposes the signals of pressure pulsation at each monitoring point so as to obtain the characteristics of various modal functions in time and frequency domains.By examining the VMD decomposition results for two monitoring points at the tail water pipe,we have found that their frequency components are similar and consistent with those monitored inside the volute.This study would provide important support for construction of intelligent hydropower stations,along with an effective means for their operation and maintenance.
作者
李常钊
王秀毓
陶智宇
方铭坤
张颖灵
孙志伟
陶然
LI Changzhao;WANG Xiuyu;TAO Zhiyu;FANG Mingkun;ZHANG Yinging;SUN Zhiwei;TAO Ran(Huadian Fuxin Zhouning Pumped Storage Co.,Ltd.,Zhouning 355400,China;College of Water Resources and Civil Engineering,China Agricultural University,Beijing 100083,China)
出处
《水力发电学报》
CSCD
北大核心
2024年第9期70-81,共12页
Journal of Hydroelectric Engineering
基金
国家自然科学基金(51909131)。
关键词
水电站
智能巡检
故障诊断
变分模态分解
图像识别
hydropower stations
intelligent inspection
fault diagnosis
variational mode decomposition
image recognition
