| 引用本文: | 闻麒,李蜀军,孙康,肖俊青,金江涛,李春,王江波,陈泳.多重分形的海上漂浮式风力机系泊状态特性分析[J].哈尔滨工业大学学报,2023,55(1):134.DOI:10.11918/202204055 |
| WEN Qi,LI Shujun,SUN Kang,XIAO Junqing,JIN Jiangtao,LI Chun,WANG Jiangbo,CHEN Yong.Multifractal analysis of mooring state characteristics of offshore floating wind turbine[J].Journal of Harbin Institute of Technology,2023,55(1):134.DOI:10.11918/202204055 |
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| 多重分形的海上漂浮式风力机系泊状态特性分析 |
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闻麒1,李蜀军1,孙康1,肖俊青1,金江涛1,李春1,2,王江波3,陈泳3
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(1．上海理工大学 能源与动力工程学院,上海 200093;2．上海市动力工程多相流动与传热重点实验室(上海理工大学),上海 200093;3．江苏东华测试技术股份有限公司,江苏 靖江 214500)
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| 摘要: |
| 漂浮式风力机因长期受风浪流作用,系泊极易发生蠕变导致腐蚀加速,增加失效概率,影响平台稳定性。为保证海上漂浮式风力机的安全运行,在系泊蠕变早期阶段实现预警,提出了基于多重分形的漂泊式风力机系泊故障诊断方法。首先,通过变分模态分解（variational mode decomposition, VMD）方法提取了系泊故障非线性信息,分析了系泊蠕变和不同位置系泊失效对漂浮式风力机稳定性的影响；其次,针对非线性信号具有多测度性特征,采用多重分形去趋势波动分析法提取故障信号特征,并判断系泊是否发生蠕变以及系泊蠕变的位置；最后,对不同位置系泊蠕变下的平台响应数据进行了对比分析。结果表明:原始信号经VMD处理,并采用分形盒维数筛选特征信号,可有效滤除噪声,提取出具有代表性的非线性特征；系泊故障信号呈多重分形特征,通过奇异指数α0可有效判断系泊蠕变及其位置；通过多重分形去趋势波动分析法分析VMD提取的非线性特征,可根据数据复杂度判断系泊状态。研究结果能够为海上漂浮式风力机系泊的信息提取和故障判断提供理论方法。 |
| 关键词: 漂浮式风力机 系泊 盒维数 多重分形 故障诊断 |
| DOI：10.11918/202204055 |
| 分类号:TK83 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(8,1)； 上海“科技创新行动计划”地方院校能力建设项目(19060502200) |
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| Multifractal analysis of mooring state characteristics of offshore floating wind turbine |
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WEN Qi1,LI Shujun1,SUN Kang1,XIAO Junqing1,JIN Jiangtao1,LI Chun1,2,WANG Jiangbo3,CHEN Yong3
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(1. School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. Shanghai Key Laboratory of Multiphase Flow and Heat Transfer for Power Engineering (University of Shanghai for Science and Technology), Shanghai 200093, China; 3. Jiangsu Donghua Testing Technology Co., Ltd., Jingjiang 214500, Jiangsu, China)
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| Abstract: |
| Under the combined actions of wind, wave, and current, the floating wind turbine is prone to creep in mooring, which can accelerate corrosion, increase the failure probability, and affect the stability of the platform. In order to ensure the safe operation of floating wind turbine and achieve early warning in the early stage of mooring creep, this paper proposes a mooring fault diagnosis method for floating wind turbine based on multifractal analysis. First, the mooring fault nonlinear information was extracted by variational mode decomposition (VMD) method, and the impact of mooring creep and mooring failure at different locations on the stability of floating wind turbine was analyzed. Then, considering the multi-metric characteristics of the nonlinear signal, multiple fractal detrended fluctuation analysis was used to extract the fault signal characteristics, and it was estimated whether mooring creep occurred and the locations of mooring creep. Finally, the platform response data under mooring creep at different locations were analyzed. Results show that when the original signal was processed by VMD and the fractal box dimension was used to filter the feature signal, the noise was effectively filtered and more representative nonlinear features were extracted. The mooring fault signal had multiple fractal features, and the mooring creep and its location were effectively estimated by the singularity index α0. The nonlinear features extracted by VMD could be estimated according to the data complexity by the multiple fractal detrended fluctuation analysis. The state of the mooring could be determined based on the complexity of the data. The research results can provide theoretical methods for information extraction and fault determination of offshore floating wind turbine moorings. |
| Key words: floating wind turbine mooring box dimension multifractal fault diagnosis |
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