摘要
水电机组在非平稳工况及异常运行状态下,会产生剧烈的振动并发出刺耳的噪声。针对上述振动和音频信号,以灯泡贯流式水电机组为研究对象,通过布置高精度的加速度和音频传感器,对机组各部位的振动和噪声进行实时监测,采集振动和音频的多源融合信号。采用核主元分析法(KPCA)与改进的K-Means聚类算法提取多源融合信号频率幅值均方根参数,得到水轮机桨叶碰磨、本体敲击及发电机局放等故障的能量分布与特征值,构建了能够反映机组状态的六维特征向量模型。现场故障模拟试验表明,该模型能准确识别出对应故障,为机组检修维护提供了有力支撑。
When a hydroelectric unit operates under non-steady working and abnormal conditions,it will produce violent vibration and harsh noises.In order to ensure the safe and reliable operation of a unit,a bulb tubular hydropower unit is taken as the research object,the vibration and noise of each unit part were monitored in real time by arranging high-precision acceleration and audio sensors,and multi-source fusion signals of vibration and audio were collected.The Kernel Principal Component Analysis(KPCA)and the improved K-Means clustering algorithm are used to extract the root mean square parameter of the frequency and amplitude of the multi-source fusion signal,and the energy distribution,eigenvalues of faults such as the turbine blade collision,body knock and generator partial discharge are obtained.Based on the energy distribution and eigenvalues,a six-dimensional eigenvector that can reflect the state of a unit is constructed.Combined with the on-site fault simulation test,the corresponding fault can be accurately identified by the extraction method.The research results can provide strong support for the maintenance of the units.
作者
陈茗
胡边
李靖
CHEN Ming;HU Bian;LI Jing(Hunan Polytechnic of Water Resources and Electric Power,Changsha 410131,China;Wuling Power Co.,Ltd.,Changsha 410004,China;Hydropower Industry Innovation Center of State Power Investment Co.,Ltd.,Changsha 410004,China;Engineering Technology Research Center of Hydropower Intelligent of Hunan Province,Changsha 410004,China)
出处
《人民长江》
北大核心
2023年第8期185-189,210,共6页
Yangtze River
基金
国家自然科学基金项目(51775185)
湖南省高新技术产业科技创新引领计划(2020GK2094)
国家电力投资集团统筹科研项目(TC2020SD01)。
