基于波动信息优选及切换输入机制的短期延长期风电集群功率预测

杨茂; 鞠超毅; 张薇; 苏欣

doi:10.19912/j.0254-0096.tynxb.2023-1776

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (3) : 546-558. DOI: 10.19912/j.0254-0096.tynxb.2023-1776

基于波动信息优选及切换输入机制的短期延长期风电集群功率预测

杨茂, 鞠超毅, 张薇, 苏欣

作者信息 +

POWER FORECAST OF WIND POWER CLUSTER IN SHORT-TERM EXTENSION BASED ON FLUCTUATION INFORMATION OPTIMIZATION AND SWITCHING INPUT MECHANISM

Yang Mao, Ju Chaoyi, Zhang Wei, Su Xin

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文章历史 +

摘要

在风电功率预测领域,现有短期时间尺度研究和应用的预见期最长为7 d,缺乏对8~15 d短期延长期时间尺度下的预测研究。针对上述问题,提出基于天气过程挖掘和切换机制的8~15 d短期延长期预测框架,着重对未来出力水平进行预测,将历史选择分为波动性优先历史选择和稳定性优先历史选择,在波动性优先历史选择效果较差时,利用稳定性优先历史选择进行误差平衡。所提框架在甘肃省某风电集群进行验证,结果表明,所提框架均方根误差在8~15 d所有时间尺度下平均降低0.84%~1.45%,在未来数值天气预报（NWP）可用性匮乏的情况下实现了8~15 d预测,有效提高短期延长期预测的可靠性。

Abstract

In the field of wind power forecast, the longest forecast period for existing short-term time scale research and applications is 7 days, and there is a lack of forecast research on the 8-15 day short-term extension period time scale. To solve the above problems, this study proposed an 8-15 day short-term extension forecast framework based on the weather process mining and switching mechanism, focusing on the forecast of future output levels, and divided historical selection into volatility first historical selection and stability first historical selection, and used stability first historical selection to balance errors when the performance of volatility first historical selection was poor. The proposed framework was verified in a wind power cluster in Gansu Province. The results show that the root-mean-square error of the proposed framework decreases by 0.84 to 1.45 percentage points on average on all timescales of 8-15 days, and the 8-15 days forecast is realized under the condition of lack of availability of NWP, and the reliability of short-term extension forecast is improved.

导出引用

杨茂, 鞠超毅, 张薇, 苏欣. 基于波动信息优选及切换输入机制的短期延长期风电集群功率预测[J]. 太阳能学报. 2025, 46(3): 546-558 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1776

Yang Mao, Ju Chaoyi, Zhang Wei, Su Xin. POWER FORECAST OF WIND POWER CLUSTER IN SHORT-TERM EXTENSION BASED ON FLUCTUATION INFORMATION OPTIMIZATION AND SWITCHING INPUT MECHANISM[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 546-558 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1776

中图分类号： TM614

参考文献

[1] 李娟, 王玲, 孙康杰, 等. 考虑风电不确定性和有功无功联合调整的两阶段优化调度[J]. 东北电力大学学报, 2023, 43(3): 72-81.
LI J, WANG L, SUN K J, et al.Two-stage optimal dispatch considering uncertainty of wind power and joint regulaiton of active and reactive power[J]. Journal of northeast electric power university, 2023, 43(3): 72-81.
[2] 黄玲, 李林霞, 程瑜, 等. 基于SAM-WGAN-GP的短期风电功率预测[J]. 太阳能学报, 2023, 44(4): 180-188.
HUANG L, LI L X, CHENG Y, et al.Short-term wind power prediction based on SAM-WGAN-GP[J]. Acta energiae solaris sinica, 2023, 44(4): 180-188.
[3] 杨茂, 张书天, 王天硕, 等. 基于IKLIEP-四分位模型的风电场异常数据识别算法[J]. 高电压技术, 2023, 49(7): 2952-2960.
YANG M, ZHANG S T, WANG T S, et al.Identification algorithm of wind farm abnormal data based on IKLIEP-quartile model[J]. High voltage engineering, 2023, 49(7): 2952-2960.
[4] 殷豪, 黄圣权, 孟安波, 等. 基于长短期记忆网络分位数回归的短期风电功率概率密度预测[J]. 太阳能学报, 2021, 42(2): 150-156.
YIN H, HUANG S Q, MENG A B, et al.Short-term wind power probability density prediction based on long short term memory network quantile regression[J]. Acta energiae solaris sinica, 2021, 42(2): 150-156.
[5] 杨茂, 于欣楠. 考虑风电场功率爬坡的超短期组合预测[J]. 东北电力大学学报, 2022, 42(1): 63-70.
YANG M, YU X N.An ultra-short term combined prediction considering wind farm power climbing[J]. Journal of northeast electric power university, 2022, 42(1): 63-70.
[6] 王晓东, 栗杉杉, 刘颖明, 等. 基于特征变权的超短期风电功率预测[J]. 太阳能学报, 2023, 44(2): 52-58.
WANG X D, LI S S, LIU Y M, et al.Ultra-short-term wind power prediction based on variable feature weight[J]. Acta energiae solaris sinica, 2023, 44(2): 52-58.
[7] 叶林, 滕景竹, 蓝海波, 等. 变尺度时间窗口和波动特征提取的短期风电功率组合预测[J]. 电力系统自动化, 2017, 41(17): 29-36, 59.
YE L, TENG J Z, LAN H B, et al.Combined prediction for short-term wind power based on variable time window and feature extraction[J]. Automation of electric power systems, 2017, 41(17): 29-36, 59.
[8] 杨茂, 刘慧宇, 孙勇, 等. 基于原子稀疏分解和支持向量机的风电功率实时预测研究[J]. 东北电力大学学报, 2020, 40(3): 1-7.
YANG M, LIU H Y, SUN Y, et al.A study on real-time prediction of wind power based on atomic sparse decomposition and support vector machine[J]. Journal of northeast electric power university, 2020, 40(3): 1-7.
[9] 王维高, 魏云冰, 滕旭东. 基于VMD-SSA-LSSVM的短期风电预测[J]. 太阳能学报, 2023, 44(3): 204-211.
WANG W G, WEI Y B, TENG X D.Short-term wind power forecasting based on VMD-SSA-LSSVM[J]. Acta energiae solaris sinica, 2023, 44(3): 204-211.
[10] 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.
[11] 杨茂, 彭天, 苏欣. 基于预测信息二维坐标动态划分的风电集群功率超短期预测[J]. 中国电机工程学报, 2022, 42(24): 8854-8864.
YANG M, PENG T, SU X.Ultra-short term wind power prediction based on two-dimensional coordinate dynamic division of prediction information[J]. Proceedings of the CSEE, 2022, 42(24): 8854-8864.
[12] YU C Q, YAN G X, YU C M, et al.A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks[J]. Energy, 2023, 263: 126034.
[13] 苗长新, 王霞, 李昊, 等. 基于数值天气预报风速误差修正的风电功率日前预测[J]. 电网技术, 2022, 46(9): 3455-3464.
MIAO C X, WANG X, LI H, et al.Day-ahead prediction of wind power based on NWP wind speed error correction[J]. Power system technology, 2022, 46(9): 3455-3464.
[14] 胡帅, 向月, 沈晓东, 等. 计及气象因素和风速空间相关性的风电功率预测模型[J]. 电力系统自动化, 2021, 45(7): 28-36.
HU S, XIANG Y, SHEN X D, et al.Wind power prediction model considering meteorological factor and spatial correlation of wind speed[J]. Automation of electric power systems, 2021, 45(7): 28-36.
[15] 欧阳庭辉, 查晓明, 秦亮, 等. 基于相似性修正的风电功率爬坡事件预测方法[J]. 中国电机工程学报, 2017, 37(2): 572-581.
OUYANG T H, ZHA X M, QIN L, et al.Wind power ramps prediction method based on amendment of similar events[J]. Proceedings of the CSEE, 2017, 37(2): 572-581.
[16] 梁志峰, 王铮, 冯双磊, 等. 基于波动规律挖掘的风电功率超短期预测方法[J]. 电网技术, 2020, 44(11): 4096-4104.
LIANG Z F, WANG Z, FENG S L, et al.Ultra-short-term forecasting method of wind power based on fluctuation law mining[J]. Power system technology, 2020, 44(11): 4096-4104.
[17] 刘燕华, 刘冲, 李伟花, 等. 基于出力模式匹配的风电集群点多时间尺度功率预测[J]. 中国电机工程学报, 2014, 34(25): 4350-4358.
LIU Y H, LIU C, LI W H, et al.Multi time scale power prediction of wind power clusters based on output pattern matching[J]. Proceedings of the CSEE, 2014, 34(25): 4350-4358.
[18] 杨茂, 许传宇, 王凯旋. 基于切换输出机制的超短期风电功率预测[J]. 高电压技术, 2022, 48(2): 420-430.
YANG M, XU C Y, WANG K X.Ultra-short-term wind power forecasting based on switching output mechanism[J]. High voltage engineering, 2022, 48(2): 420-430.
[19] 戴千斌, 黄南天. 短期风电功率预测误差修正研究综述[J]. 东北电力大学学报, 2023, 43(2): 1-7.
DAI Q B, HUANG N T.Review of research on error correction of short-term wind power prediction[J]. Journal of northeast electric power university, 2023, 43(2): 1-7.
[20] 杨茂, 代博祉, 刘蕾. 风电功率概率预测研究综述[J]. 东北电力大学学报, 2020, 40(2): 1-6.
YANG M, DAI B Z, LIU L.A review of wind power probabilistic prediction[J]. Journal of Northeast Electric Power University, 2020, 40(2): 1-6.
[21] 张淑清, 杜灵韵, 王册浩, 等. 基于格拉姆角场与改进CNN-ResNet的风电功率预测方法[J]. 电网技术, 2023, 47(4): 1540-1548.
ZHANG S Q, DU L Y, WANG C H, et al.Wind power forecasting method based on GAF and improved CNN-Res Net[J]. Power system technology, 2023, 47(4): 1540-1548.
[22] 李卓, 叶林, 戴斌华, 等. 基于IDSCNN-AM-LSTM组合神经网络超短期风电功率预测方法[J]. 高电压技术, 2022, 48(6): 2117-2127.
LI Z, YE L, DAI B H, et al.Ultra-short-term wind power prediction method based on IDSCNN-AM-LSTM combination neural network[J]. High voltage engineering, 2022, 48(6): 2117-2127.
[23] 杨健, 徐思卿, 姜尚光, 等. 基于动态时间规整的风电功率爬坡滚动修正模型[J]. 电力系统自动化, 2021, 45(16): 152-159.
YANG J, XU S Q, JIANG S G, et al.Rolling correction model of ramp for wind power based on dynamic time warping[J]. Automation of electric power systems, 2021, 45(16): 152-159.
[24] 吴振军, 冯为民, 胡智宏, 等. 基于改进Simpson积分算法的电能计量方法[J]. 电力系统保护与控制, 2012, 40(13): 14-17, 23.
WU Z J, FENG W M, HU Z H, et al.Method of electric energy measurement based on modified Simpson integral algorithm[J]. Power system protection and control, 2012, 40(13): 14-17, 23.
[25] 余光正, 陆柳, 汤波, 等. 考虑转折性天气的海上风电功率超短期分段预测方法研究[J]. 中国电机工程学报, 2022, 42(13): 4859-4871.
YU G Z, LU L, TANG B, et al.Research on ultra-short-term subsection forecasting method of offshore wind power considering transitional weather[J]. Proceedings of the CSEE, 2022, 42(13): 4859-4871.
[26] 杨茂, 白玉莹. 基于多位置NWP和门控循环单元的风电功率超短期预测[J]. 电力系统自动化, 2021, 45(1): 177-183.
YANG M, BAI Y Y.Ultra-short-term prediction of wind power based on multi-location numerical weather prediction and gated recurrent unit[J]. Automation of electric power systems, 2021, 45(1): 177-183.