摘要
传统的超短期风速预测方法往往采用风电场内单一位置处风速信号进行预测,忽略了风电机组间的风速相关性,导致预测模型难以考虑地形和尾流影响下的风速空间分布特征,限制了超短期风速预测精度的提高。因此,提出了一种基于深度卷积循环神经网络的风电场多点位风速超短期预测方法,考虑风速时空相关性进行风速空间分布的超短期预测。提出的方法结合了卷积神经网络和长短期记忆网络,采用卷积神经网络获取长期风速空间分布特征,利用长短期记忆模型获取短期时间序列特征,可以同时获得多个点位处的风速超短期预测结果。通过山东某风电场数据的验证:与传统方法相比,所提模型的精度均有所提升;将测试集按季度分开,在各模型预测结果均最好的第四季度,对于多点位的平均误差水平来说,所提模型的平均绝对误差和均方根误差为0.367m/s和0.506m/s,比持续法分别降低了15.0%和15.2%,比支持向量机模型分别降低了31.5%和43.1%。
The traditional ultra-short-term wind speed prediction often utilizes the wind speed signals at a single location within the wind farm, ignoring the correlation of the wind speed among wind turbines. It is difficult to consider the spatial distribution characteristics of the wind speed under the influence of terrain and wake, thus affecting the improvement of ultra-short-term wind speed prediction accuracy. Therefore, a multi-location ultra-short-term wind speed prediction method based on the combination of Recurrent and Convolutional Neural Networks is proposed in this paper, which can predict wind speed spatial distribution under consideration of the spatial and temporal correlation of wind speed. The proposed method combines the convolutional neural network and the long-short term memory network. The convolutional neural network is employed to obtain long-term wind speed spatial distribution characteristics, while the long-short term memory network is used to get the short-term time series features. With these two networks combined, this method can finally produce the multi-location ultra-short-term wind speed prediction results simultaneously. With the data of a wind farm in Shandong province as an example, it is proved that the accuracy of the proposed model is improved compared with the traditional methods. The test set is divided according to the quarters of a year. In the fourth quarter where the prediction results of all models are the best, for the average error level of multiple points, the MAE and RMSE of the proposed model are 0.367 m/s and 0.506 m/s, which are respectively reduced by 15.0% and 15.2% than the persistence method model, and reduced by 31.5% and 43.1% than the SVM model.
作者
梁超
刘永前
周家慷
阎洁
鲁宗相
LIANG Chao;LIU Yongqian;ZHOU Jiakang;YAN Jie;LU Zongxiang(State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources(North China Electric Power University),Changping District,Beijing 102206,China;Guodian Technology&Environment Group Corporation Limited,Haidian District,Beijing 100036,China;Department of Electrical Engineering,Tsinghua University,Haidian District,Beijing 100084,China)
出处
《电网技术》
EI
CSCD
北大核心
2021年第2期534-541,共8页
Power System Technology
基金
国家重点研发计划项目(2016YFB0900100)
国家自然科学基金项目(U1765201)。
关键词
超短期预测
卷积神经网络
长短期记忆网络
多点位
风速空间分布
ultra-short-term prediction
convolutional neural network
long-short term memory network
multi-locations
wind speed spatial distribution
