考虑局部条件特征的风电功率短期预测

张家安; 黄晨旭; 李志军

doi:10.19912/j.0254-0096.tynxb.2023-1183

PDF(1286 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (12) : 220-227. DOI: 10.19912/j.0254-0096.tynxb.2023-1183

考虑局部条件特征的风电功率短期预测

张家安^1,2, 黄晨旭², 李志军^1,2

作者信息 +

SHORT-TERM PREDICTION OF WIND POWER CONSIDERING LOCAL CONDITION FEATURES

Zhang Jiaan^1,2, Huang Chenxu², Li Zhijun^1,2

Author information +

文章历史 +

摘要

提出一种考虑局部条件特征的风电功率短期预测方法。首先,基于斯皮尔曼相关系数对局部条件因素与风力机功率的相关性进行分析,确定风速、风向和对风角度等为影响风电场功率短期预测准确度的关键因素;然后,基于广义极值分布分别对关键因素的分布参数进行估计,并构建平均波动系数指标描述各风力机间的参数差异性,基于K-means++算法对风力机进行聚类;最后,采用主成分分析（PCA）方法提取机群内各风力机功率的关键特征,并基于双向循环神经网络（BiGRU）对机群功率进行预测,进而累加获取风电场的预测功率。以华北某风电场运行数据为算例,验证该方法的有效性。

Abstract

A short-term prediction method of wind power considering local condition features is proposed. Firstly, based on the Spearman correlation coefficient, the correlation between local condition factors and wind turbine power is analyzed. Wind speed, wind direction together with yaw angle are selected as key factors. Then, the distribution parameters of key factors are estimated separately with the generalized extreme value distribution, and an average fluctuation coefficient index is constructed to describe the parameter differences between each wind turbine. The wind turbines are clustered into several groups with the K-means++ algorithm. Finally, the key features of each wind turbine cluster are extracted with principal component analysis （PCA）. Based on Bidirectional gated recurrent units （BiGRU）, the power of the cluster is accurately predicted and accumulated. Taking the operation data of a wind farm in North China as an example, the effectiveness of this method is verified.

导出引用

张家安, 黄晨旭, 李志军. 考虑局部条件特征的风电功率短期预测[J]. 太阳能学报. 2024, 45(12): 220-227 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1183

Zhang Jiaan, Huang Chenxu, Li Zhijun. SHORT-TERM PREDICTION OF WIND POWER CONSIDERING LOCAL CONDITION FEATURES[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 220-227 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1183

中图分类号： TM614

参考文献

[1] 魏泓屹, 卓振宇, 张宁, 等. 中国电力系统碳达峰·碳中和转型路径优化与影响因素分析[J]. 电力系统自动化, 2022, 46(19): 1-12.
WEI H Y, ZHUO Z Y, ZHANG N, et al.Transition path optimization and influencing factor analysis of carbon emission peak and carbon neutrality for power system of China[J]. Automation of electric power systems, 2022, 46(19): 1-12.
[2] DAI X R, LIU G P, HU W S.An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting[J]. Energy, 2023, 272: 127173.
[3] WEI J Q, WU X J, YANG T M, et al.Ultra-short-term forecasting of wind power based on multi-task learning and LSTM[J]. International journal of electrical power & energy systems, 2023, 149: 109073.
[4] ZHOU X, LIU C X, LUO Y J, et al.Wind power forecast based on variational mode decomposition and long short term memory attention network[J]. Energy reports, 2022, 8: 922-931.
[5] XIAO Y L, ZOU C Z, CHI H T, et al.Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis[J]. Energy, 2023, 267: 126503.
[6] ZHAO Z N, YUN S N, JIA L Y, et al.Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features[J]. Engineering applications of artificial intelligence, 2023, 121: 105982.
[7] 董雪, 赵宏伟, 赵生校, 等. 基于SOM聚类和二次分解的BiGRU超短期光伏功率预测[J]. 太阳能学报, 2022, 43(11): 85-93.
DONG X, ZHAO H W, ZHAO S X, et al.Ultra-short-term forecasting method of photovoltaic power based on som clustering, secondary decomposition and BiGRU[J]. Acta energiae solaris sinica, 2022, 43(11): 85-93.
[8] LIU L, LIU J C, YE Y, et al.Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty[J]. Renewable energy, 2023, 205: 598-607.
[9] 李大中, 李颖宇. 基于深度学习与误差修正的超短期风电功率预测[J]. 太阳能学报, 2021, 42(12): 200-205.
LI D Z, LI Y Y.Ultra-short term wind power prediction based on deep learning and error correction[J]. Acta energiae solaris sinica, 2021, 42(12): 200-205.
[10] YU M, NIU D X, GAO T, et al.A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism[J]. Energy, 2023, 269: 126738.
[11] DONG W C, SUN H X, LI Z, et al.Short-term wind-speed forecasting based on multiscale mathematical morphological decomposition, K-means clustering, and stacked denoising autoencoders[J]. IEEE access, 2020, 8: 146901-146914.
[12] 张思毅, 刘明波, 雷振兴, 等. 基于集合经验模态分解和编码器-解码器的风电功率多步预测[J]. 南方电网技术, 2023, 17(4): 16-24.
ZHANG S Y, LIU M B, LEI Z X, et al.Multi-step prediction of wind power based on ensemble empirical mode decomposition and encoder-decoder[J]. Southern power system technology, 2023, 17(4): 16-24.
[13] 王勃, 刘纯, 冯双磊, 等. 基于集群划分的短期风电功率预测方法[J]. 高电压技术, 2018, 44(4): 1254-1260.
WANG B, LIU C, FENG S L, et al.Prediction method for short-term wind power based on wind farm clusters[J]. High voltage engineering, 2018, 44(4): 1254-1260.
[14] WANG F, CHEN P, ZHEN Z, et al.Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method[J]. Applied energy, 2022, 323: 119579.
[15] 王一妹, 刘辉, 宋鹏, 等. 基于高斯混合模型聚类的风电场短期功率预测方法[J]. 电力系统自动化, 2021, 45(7): 37-43.
WANG Y M, LIU H, SONG P, et al.Short-term power forecasting method of wind farm based on Gaussian mixture model clustering[J]. Automation of electric power systems, 2021, 45(7): 37-43.
[16] 黄坤鹏. 风电功率超短期预测及可用发电功率计算[D]. 北京: 北京交通大学, 2022.
HUANG K P.Ultra-short-term wind power forecasting and available wind power calculation[D]. Beijing: Beijing Jiaotong University, 2022.
[17] 李远征. 基于深度学习的超短期风电功率预测研究[D]. 南昌: 华东交通大学, 2021.
LI Y Z.Research on ultra-short-term wind power prediction based on deep learning[D]. Nanchang: East China Jiaotong University, 2021.
[18] 陈天阳, 钱政, 荆博, 等. 基于K-means++与ELM的短期风电功率预测模型研究[J]. 电测与仪表, 2024, 61(6): 45-50.
CHEN T Y, QIAN Z, JING B, et al.Research on short-term wind power forecasting model based on K-means++ and ELM[J]. Electrical measurement & instrumentation, 2024, 61(6): 45-50.
[19] GU B, HU H, ZHAO J, et al.Short-term wind power forecasting and uncertainty analysis based on FCM-WOA-ELM-GMM[J]. Energy reports, 2023, 9: 807-819.
[20] 杨子民, 彭小圣, 郎建勋, 等. 基于集群动态划分与BLSTM深度学习的风电集群短期功率预测[J]. 高电压技术, 2021, 47(4): 1195-1203.
YANG Z M, PENG X S, LANG J X, et al.Short-term wind power prediction based on dynamic cluster division and BLSTM deep learning method[J]. High voltage engineering, 2021, 47(4): 1195-1203.
[21] 郑祚芳, 范水勇, 高华. 风向偏差角对风机功率预测的影响[J]. 电网与清洁能源, 2022, 38(5): 104-108, 119.
ZHENG Z F, FAN S Y, GAO H.The influence of wind direction deviation angle on wind power prediction[J]. Power system and clean energy, 2022, 38(5): 104-108, 119.
[22] CHEN H L, ZHAO T.Modeling power loss during blackouts in China using non-stationary generalized extreme value distribution[J]. Energy, 2020, 195: 117044.
[23] COUTO A, ESTANQUEIRO A.Enhancing wind power forecast accuracy using the weather research and forecasting numerical model-based features and artificial neuronal networks[J]. Renewable energy, 2022, 201: 1076-1085.