针对水电机组动态气隙监测中产生的强噪声、尖峰特性和非高斯等复杂信号，将分数阶理论引入到机组动态信息融合处理中，通过分析定转子不同位置处传感器动态气隙的变化特性和信息相关性，提出并建立了动态气隙监测的多时间尺度分数阶信息融合法。结合机组运行和振动的数值仿真分析，针对不同噪声和故障信号的动态气隙传感器信号进行了不同时间尺度下的分数阶信息融合处理，并与传统的卡尔曼滤波结果进行了比较，充分验证了该方法的有效性和可靠性。该理论方法的研究进一步充实了水电机组状态监测和故障诊断的系统理论，也为复杂信号的处理分析提供了新的思路和方法。
Abstract
Aiming at the complex signals such as strong noise，peak characteristics and non-Gaussian generated in the dynamic air gap monitoring of hydropower units, the fractional order theory is introduced into the dynamic information fusion processing of hydropower units. By analyzing the change characteristics and information correlation of dynamic air gap sensors at different positions of the stator and rotor, a multi-time scale fractional order information fusion method for dynamic air gap monitoring is proposed and established. Combined with the numerical simulation analysis of unit operation and vibration, the fractional information fusion processing under different time scales is carried out for the dynamic air gap sensor signals with different noise and fault signals, and the results are compared with those of traditional Kalman filter, which fully verifies the effectiveness and reliability of the proposed method. The research enriches the system theory of condition monitoring and fault diagnosis of hydropower units, and provides a new idea and method for the processing and analysis of complex signals.
关键词
水轮发电机 /
气隙 /
分数阶 /
信息融合 /
多尺度分析
{{custom_keyword}} /
Key words
hydro generator /
air gap /
fractional order /
information fusion /
multi-scale analysis
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 周建中, 张勇传, 李超顺. 水轮发电机组动力学问题及故障诊断原理与方法[M]. 武汉：华中科技大学出版社, 2013.
Zhou Jian-zhong, Zhang Yong-chuan, Li Chao-shun. Dynamics problems and fault diagnosis principles and methods of hydroelectric generating units [M]. Wuhan: Huazhong University of Science and Technology Press, 2013.
[2] 何玉灵，万书亭，唐贵基，等. 基于定子振动特性的汽轮发电机气隙偏心故障程度鉴定方法研究[J]. 振动与冲击, 2012, 31(22): 53-57，89.
HE Yu-ling，WAN Shu-ting，TANG Gui-ji，et al. Eccentric fault level identification of a turbo-generator based on stator vibration characteristics [J]. Journal of Vibration and Shock, 2012, 31(22): 53-57，89.
[3] Li Cheng, Yan Junwei, Rong Ruchao, et al. Research on Air Gap Monitoring Technology of Hydro-Generator Based on Infrared Monocular Visual Image Analysis [C]. 2023 5th Asia Energy and Electrical Engineering Symposium, AEEES 2023. Chengdu, China：IEEE. 2023: 133-138.
[4] Simon Bernier, Arezki Merkhouf, Kamal Al-Haddad. Stray flux and air gap flux experimental measurement and analysis in large hydro generators [C]. 2023 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD). Newcastle upon Tyne, United Kingdom: IEEE. 2023.
[5] Levitskyi, А.S. Rassovskyi, V.L. Rassovskyi, V.L. A capacitive sensor with parallel complanare electrodes system for measurement of air gap in hydrogenerators [J]. Technical Electrodynamics, 2023,(3): 80-85.
[6] 桂中华，潘罗平，陆力. 水电机组状态监测与故障诊断研究新进展[J]. 中国水利水电科学研究院学报，2009, 7(3): 179-185.
Gui ZhonghuaPan LuopingLu Li. Advances in condition monitoring and fault diagnosis research of hydropower generator units [J]. Journal of China Institute of Water Resources and Hydropower Research，2009, 7(3): 179-185.
[7] 卢旺城, 王家祥. 水轮发电机气隙测量及其滤波分析[J]. 机电技术, 2022, (6): 58-60.
Lu Wangcheng, Wang Jiaxiang. Air gap measurement and filter analysis of hydrogenerator [J]. Mechanical & Electrical Technology, 2022, (6): 58-60.
[8] 王静静，秦刚，李腾. 融合卡尔曼改进小波的姿态传感器滤波方法[J]. 仪表与自动化装置, 2023,38(9): 110-119.
Wang Jingjing, Qin Gang, Li Teng. Attitude Sensor Filtering Method Based on Kalman’ s Improved Wavelet [J]. Automation ＆ Instrumentation, 2023,38(9): 110-119.
[9] 盛虎. 分数阶信号合成与分数阶滤波技术[M]. 北京：科学出版社，2022.
Sheng Hu. Fractional Signal synthesis and fractional filtering techniques [M]. Beijing: Science Press, 2022.
[10] 冼剑华，苏成. 分数阶导数系统非平稳随机振动灵敏度分析的时域显式方法[J]. 振动工程学报, 2022, 35(5): 1058-1066.
XIAN Jian⁃hua，SU Cheng. Explicit time - domain method for sensitivity analysis of nonstationary random vibration of systems with fractional derivatives [J]. Journal of Vibration Engineering, 2022, 35(5): 1058-1066.
[11] 王世元，史春芬，钱国兵，等. 基于分数阶最大相关熵算法的混沌时间序列预测[J]. 物理学报, 2018,57(1): 018401.
Wang Shi-Yuan, Shi Chun-Fen, Qian Guo-Bing, et al. Prediction of chaotic time series based on the fractional-order maximum correntropy criterion algorithm [J]. Acta Phys. Sin. 2018,57(1): 018401.
[12] 周激流，蒲亦非，廖科. 分数阶微积分原理及其在现代信号分析与处理中的应用[M]. 北京：科学出版社，2010.
Zhou Jiliu, Pu Yifei, Liao Ke. Principle of fractional Calculus and its application in Modern Signal Analysis and Processing [M]. Beijing: Science Press, 2010.
[13] 马瑞群，张 波，员海玮，等. 基于Grünwald-Letnikov定义改进的短记忆原理方法[J]. 振动与冲击, 2022, 41(10): 215-220.
MA Ruiqun，ZHANG Bo，YUAN Haiwei，et al. Improved short memory principle method based on the Grünwald-Letnikov definition[J]. Journal of Vibration and Shock, 2022, 41(10): 215-220.
[14] 左延红，程 桦，朱银锋. 基于分数阶微分的多传感器检测数据融合算法[J]. 科学技术与工程，2019, 19(11): 188-193.
ZUO Yan-hong, CHENG Hua, ZHU Yin-feng. The Algorithm for Multi-sensors Detection Data Fusion Based on Fractional Differential [J]. Science Technology and Engineering，2019, 19(11): 188-193.
[15] Sun W Q, Wang B X, Ma Z Y, et al. Insights into the nonlinear dynamic characteristics of a water turbine generator set considering the coupled effects of a fractional-order broadband foundation [J]. Journal of Sound and Vibration, 2024, 573: 118190.
[16] 孙万泉, 张 宁. 基于MRD的水轮发电机组轴系非线性振动控制[J]. 振动与冲击, 2020, 39(1): 79-84.
SUN Wanquan, ZHANG Ning. Nonlinear vibration control for hydro-generator unit’s shafting based on MRD [J]. Journal of Vibration and Shock, 2020, 39(1): 79-84.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(3470 KB)
