现有风力发电机组分析主要考虑风速的波动性和随机性引起的转速随机波动。但在实际运行中,由于等效风速引起的输入转矩周期性波动,使风力发电机组的瞬时转速中同时含有随机波动和周期性波动成分。该文基于风力发电机组传动系统振动信号研究其瞬时转速波动特性曲线提取,首先,分析风力发电机组叶轮的瞬时转速波动特性及其在振动信号中的表现;然后,采用同步提取变换算法（SET）获得信号的时频分布并利用脊线搜索算法提取多条时频脊线;最后,通过脊线融合算法对提取的瞬时频率时频脊线进行脊线融合,实现风力发电机组瞬时转速提取与优化,并进一步分析风力发电机组瞬时转速的周期性波动特性。仿真和实验均表明,该方法能够在无转速计的情况下从振动信号中有效提取风力发电机组瞬时转速波动特性曲线,并分析瞬时转速变化特点以及此特点所能映射的转速变化成因。
Abstract
The analysis of existing wind turbines mainly considers the fluctuation of wind speed and the random fluctuation of speed caused by randomness. However, in actual operation, due to the periodic fluctuation of input torque caused by equivalent wind speed, the instantaneous speed of wind turbines contains both random and periodic fluctuation components. This article studies the instantaneous speed fluctuation characteristic curve extraction based on the vibration signal of the wind turbine transmission system. Firstly, the instantaneous speed fluctuation characteristics of the wind turbine impeller and its performance in the vibration signal are analyzed. Then, the synchroextracting transform （SET） algorithm is used for time-frequency analysis, and time-frequency ridges are extracted based on the local peak search algorithm. Finally, the ridge fusion algorithm is used to fuse the extracted instantaneous frequency ridges, achieving the extraction and optimization of wind turbine instantaneous speed. The periodic fluctuation characteristics of wind turbine instantaneous speed are further analyzed. Both simulation and experiments have shown that this method can effectively extract the instantaneous speed fluctuation characteristic curve of wind turbines from vibration signals without a tachometer, and analyse the characteristics of instantaneous rotational speed change and the causes of rotational speed change that this characteristic can be mapped.
关键词
风力发电机组 /
风速 /
振动分析 /
脊线融合 /
瞬时转速波动特性
Key words
wind turbines /
wind speed /
vibration analysis /
ridge fusion /
instantaneous speed fluctuation characteristics
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基金
国家自然科学基金（52275109）;河北省自然科学基金（E2022502007）;保定市科技计划基础研究专项基金（2172P010）;河北省研究生创新能力培养资助项目（CXZZBS2023151）
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