摘要
实现水电机组状态劣化评估和故障预警是行业研究的热点。论文提出了一种结合时域与频域特征的机组劣化在线评估方法。(1)先利用检测指数确定振动信号中对机组运行状态最为敏感的时域特征;再以机组健康状态下工况参数X(水头、开度等)和检测指数筛选的振动信号时域特征Y为健康样本,利用最小二乘支持向量机构建机组状态健康模型Y=f(X)。基于该模型,以实时工况参数为输入,在线预测对应工况下机组振动信号时域特征健康值,计算健康值与实际值之间的相对误差,作为评估机组劣化程度的时域劣化指标。(2)利用小波变换与奇异值理论对振动信号进行分解,提取健康状态下机组振动信号奇异值特征向量并得到健康聚类中心,实时计算实测信号奇异值特征向量与健康聚类中心之间的相对欧式距离,作为频域劣化指标。结合时域和频域劣化指标,在线计算综合劣化指标评估当前时刻机组劣化程度。结合实际机组运行案例,验证了该模型的有效性和实用性。
Realizing the deterioration assessment and the fault early warning of hydropower units is a hot topic in the industry.In this paper,an online evaluation method for unit degradation combining time do⁃main and frequency domain features is proposed.(1)The detection index is used to determine the time do⁃main characteristics of the vibration signal that are most sensitive to the operating state of the unit.The op⁃erating parameter X(head,opening,etc.)of the unit in health state and the time domain characteristics Y of the vibration signal selected by the detection index are selected as healthy sample,and the least squares support vector machine is used to construct the unit state health model Y=f(X).The real-time operating condition parameters are inputted into this model,and the health value of the unit's vibration sig⁃nal time-domain characteristic under corresponding operating conditions is predicted online.The relative er⁃ror between the health value and the actual value is calculated as a time-domain degradation index.(2)The wavelet transform and singular value theory are used to decompose the vibration signal to extract the singular value feature vector of the unit's vibration signal under healthy state and obtain the health cluster⁃ing center.The relative Euclidean distance is used as an index of frequency domain degradation.Combined with the time and frequency domain degradation indicators,an online calculation of comprehensive degrada⁃tion indicators is performed to assess the degree of unit degradation at the current moment.Combined with actual unit operation cases,the effectiveness and practicability of the model are verified.
作者
刘东
赖旭
胡晓
肖志怀
LIU Dong;LAI Xu;HU Xiao;XIAO Zhihuai(State Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University,Wuhan 430072,China;Key Laboratory of Hydraulic Machinery Transients,Ministry of Education,Wuhan University,Wuhan 430072,China)
出处
《水利学报》
EI
CSCD
北大核心
2021年第4期461-473,共13页
Journal of Hydraulic Engineering
基金
国家自然科学基金项目(51979204,51379160)。
关键词
水电机组
劣化评估
振动信号
检测指数
最小二乘支持向量机
小波奇异值
hydropower unit
degradation assessment
vibration signal
detection index
least squares sup⁃port vector machine
wavelet singular value
