基于激光测风雷达及SSA-ELM的风电场短期风速预测
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引用本文:王喜泉1,何山1,2,王维庆1,2,孔令清1,陈伟1.基于激光测风雷达及SSA-ELM的风电场短期风速预测[J].电网与清洁能源,2022,38(5):120~128
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作者单位
王喜泉1 1. 新疆大学 电气工程学院 
何山1,2 1. 新疆大学 电气工程学院2. 可再生能源发电与并网控制教育部工程研究中心 
王维庆1,2 1. 新疆大学 电气工程学院2. 可再生能源发电与并网控制教育部工程研究中心 
孔令清1 1. 新疆大学 电气工程学院 
陈伟1 1. 新疆大学 电气工程学院 
基金项目:国家自然科学基金(51767024;51667020);新疆维吾尔自治区高校科研计划项目(XJEDU2021I010)
中文摘要:基于激光测风雷达数据,针对风速的非线性特性,提出麻雀搜索算法(sparrow search algorithm, SSA)优化极限学习机(extreme learning machine,ELM)进行风速预测。搭建预测模型,根据预测风速对风电机组进行预变桨,分析风电机组叶根矩载荷。采用新疆某风电场激光测风雷达数据仿真并与其他预测模型分析对比。结果表明,麻雀算法优化的极限学习机可精确预测风速,且显著提升极限学习机预测速度及不同风速条件下的动态性能;预变桨后,风电机组叶根矩载荷大幅减小,提升了桨叶使用寿命及运行安全性。
中文关键词:激光测风雷达  麻雀优化算法  极限学习机  预变桨
 
Short-Term Wind Speed Prediction of Wind Farms Based on Laser Wind Measurement Radar and SSA-Elm
Abstract:Based on the laser wind radar data, and aiming at the nonlinear characteristics of wind speed, the Sparrow Search Algorithm (SSA) is proposed in this paper to optimize the Extreme Learning Machine (ELM) for wind speed forecast. Based on the forecast model established, the pre-pitch is performed according to the predicted wind speed, and the moment load of the wind turbine blade root is analyzed. The simulation is carried out based on the laser radar data of the wind speed measurement of a wind farm in Xinjiang and comparison is made with other prediction models. The results show that the extreme learning machine optimized by Sparrow can accurately predict the wind speed, and significantly improve the prediction speed of the extreme learning machine and the dynamic performance under different wind speed conditions; after the pre-pitch, the blade root moment load of the wind turbine is greatly reduced, which improves the service life and operational safety of the blades.
keywords:laser wind measurement radar  sparrow search algorithm  extreme learning machine  pre-pitch
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