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风电变流技术
★ 5.0
复杂风电场中风机来流风速的分析与预测:考虑气象因素及风电场时空特性
Analysis and prediction of incoming wind speed for turbines in complex wind farm: Accounting for meteorological factors and spatiotemporal characteristics of wind farm
| 作者 | Hongkun Lu · Xiaoxia Gao · Jinxiao Yu · Qiansheng Zhao · Xiaoxun Zhu · Wanli Ma · Jingyuan Cao · Yu Wang |
| 期刊 | Applied Energy |
| 出版日期 | 2025年1月 |
| 卷/期 | 第 381 卷 |
| 技术分类 | 风电变流技术 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | The proposed model predicts incoming wind speed for turbines in complex wind farms. |
语言:
中文摘要
摘要 预测和计算风机轮毂前方的来流风速是风电功率预测研究中的关键环节。本文提出了一种考虑气象空间环境、风速时间特性以及地形和风机尾流效应的风电机组来流风速预测方法。首先,采用气象空间降尺度与时间特征提取方法对风气象桅杆(WMM)处的风速进行预测,建立大尺度气象背景与WMM风速之间的时空关联关系;其次,利用WMM预测风速,并结合从WMM到特定风电机组路径上的地形影响和尾流效应,计算该机组的来流风速;第三,利用激光雷达(LiDAR)在中国张家口张北某风电场的一台特定风电机组上对本文所提方法进行了验证,并对实验结果进行了全面分析。结果表明,本文提出的方法能够准确预测风电机组前方的实际来流风速。对未来四天逐小时单步来流风速的预测结果显示,实际值与预测值之间的平均绝对误差(MAE)、均方根误差(RMSE)、决定系数(R²)和平均绝对百分比误差(MAPE)分别为0.6173 m/s、0.7958 m/s、0.9432和8.466%。本文提出的来流风速预测方法可为风电功率预测提供参考依据。
English Abstract
Abstract Predicting and calculating the incoming wind speed ahead of the turbine hub is a crucial aspect of research into wind power forecasting . This paper proposes a method for predicting wind turbine incoming wind speeds, which considers the meteorological spatial environment, the temporal characteristics of wind speeds, and the effects of topography and wind turbine wake. Firstly, the Wind Meteorological Mast (WMM) wind speed is predicted using the meteorological spatial downscaling and temporal feature extraction methods, which establishes a spatial and temporal relationship between the mesoscale meteorological background and wind speeds at WMM. Secondly, the incoming wind turbine speed is calculated using the WMM-predicted wind speeds, along with topography and wake effects from the WMM to the specific wind turbine are taken into consideration. Thirdly, the performance of the method proposed in this paper was validated using LiDAR for a special wind turbine at a wind farm in Zhangbei, China, and the resulting experimental findings have been subjected to comprehensive analysis. Results indicate that the method presented in this paper can accurately predict the actual incoming wind speed in front of the wind turbine. The hourly single-step incoming wind speed predictions for the subsequent four days indicate that the discrepancies between the actual and predicted incoming wind speed of the MAE, RMSE, R 2 , and MAPE are 0.6173 m/s, 0.7958 m/s, 0.9432, and 8.466 %, respectively. The incoming wind speed predict method presented in this paper can serve as a reference for wind power prediction.
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SunView 深度解读
该风速预测技术对阳光电源风电变流器及储能系统具有重要应用价值。通过融合气象降尺度、时空特征提取和尾流效应建模,可显著提升风功率预测精度(R²达0.9432)。该方法可集成至iSolarCloud平台,为风储耦合系统提供精准预测支持:1)优化ST系列储能PCS的充放电策略,提前响应风电波动;2)改进GFM/VSG控制算法的前馈补偿;3)增强PowerTitan储能系统在风电场景的平抑能力。建议结合LiDAR实测数据开发预测性维护功能,提升风电场智能运维水平。