| 引用本文: | 兰朝凤,苏文涛,李小斌,李凤臣,赵昊阳.水轮机压力脉动的混沌动力学特性[J].哈尔滨工业大学学报,2016,48(7):101.DOI:10.11918/j.issn.0367-6234.2016.07.016 |
| LAN Chaofeng,SU Wentao,LI Xiaobin,LI Fengchen,ZHAO Haoyang.Study on the chaotic dynamic characteristics of pressure fluctuations in hydro-turbine[J].Journal of Harbin Institute of Technology,2016,48(7):101.DOI:10.11918/j.issn.0367-6234.2016.07.016 |
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| 水轮机压力脉动的混沌动力学特性 |
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兰朝凤1,2, 苏文涛3, 李小斌1, 李凤臣1, 赵昊阳4
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(1. 哈尔滨工业大学 能源科学与工程学院, 哈尔滨 150001; 2. 哈尔滨理工大学 电气与电子工程学院, 哈尔滨 150080; 3. 哈尔滨工业大学 经济与管理学院, 哈尔滨 150001; 4. 水力发电设备国家重点实验室(哈尔滨大电机研究所), 哈尔滨 150040)
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| 摘要: |
| 为有效进行水轮机的运行监测和故障诊断，采用混沌动力学方法对水轮机在偏工况运行时的压力脉动特征进行研究. 利用提升小波变换对压力脉动实验数据进行去噪，给出压力脉动信号时域分布及各频率成分的能量分布情况. 分析脉动信号由轻度空化发展到严重空化过程中的一系列动力学特性，包括时频特征分布、相轨迹图、李雅普诺夫(Lyapunov)指数图和庞加莱(Poincaré)映射图等. 结果表明：水轮机尾水管压力脉动的主要能量分布在低频段，随着空化程度的加重，频谱脉动强度增大；对于研究工况，最大Lyapunov指数均大于零且随空化程度增加而增大，说明水轮机压力脉动信号存在混沌吸引子. 利用本文方法进行多工况分析，可完成在线运行监测.
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| 关键词: 水轮机 压力脉动 混沌 提升小波变换 Lyapunov指数 |
| DOI：10.11918/j.issn.0367-6234.2016.07.016 |
| 分类号:O415.5 |
| 文献标识码:A |
| 基金项目:中央高校基本科研业务费专项资金(HIT.NSRIF201667); 黑龙江省自然科学基金青年基金(QC2015082) |
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| Study on the chaotic dynamic characteristics of pressure fluctuations in hydro-turbine |
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LAN Chaofeng1,2, SU Wentao3, LI Xiaobin1, LI Fengchen1, ZHAO Haoyang4
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(1.School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China; 2.School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China; 3.School of Management, Harbin Institute of Technology, Harbin 150001, China; 4.State Key Laboratory of Hydropower Equipment(Harbin Institute of Large Electrical Machinery), Harbin 150040, China)
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| Abstract: |
| To effectively conduct the operating monitoring and malfunction detection of hydro-turbine, this paper investigated the pressure fluctuation characteristics of hydro-turbine running at partial flow conditions, by using the chaos dynamics. Quantitative information of experimental data was obtained. For the pressure fluctuation data monitoring at draft tube, the lifting wavelet transform was adopted to perform the de-noising, hereby, the fluctuation signal distribution on the frequency domain, the energy changing, and the energy partition accounting for the total energy was calculated. Then, for the flow conditions ranging from no cavitation to severe cavitation, the chaos dynamic features of fluctuation signals were analyzed, including the temporal-frequency distribution, phase trajectory, Lyapunov exponent and Poincaré etc.. It is revealed that, the main energy of pressure fluctuations in the draft tube locates at low-frequency region. As the cavitation grows, the amplitude of power spectrum at frequency domain becomes larger. For all the flow conditions, all the maximum Lyapunov exponents are larger than zero, and they increase as well. Therefore, it is believed that there indeed exist the chaotic attractors in the pressure fluctuation signals. Based on the multi-condition analysis, the on-line operating monitoring can be accomplished.
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| Key words: hydro-turbine pressure fluctuations chaos lifting wavelet transform Lyapunov exponent |
