基于爬坡特征与改进PRAA的深远海风电功率短期预测研究
CSTR:
作者:
作者单位:

(1.上海电力大学电气工程学院,上海 200090;2.国网冀北电力有限公司张家口供电公司,河北 张家口 075000;3.上海海洋大学信息学院,上海 201306;4.华能(浙江)能源开发有限公司清洁能源分公司,浙江 杭州 310014)

通讯作者:

时 帅(1987—),男,博士,讲师,主要从事风电并网可靠性研究;E?mail: shishuai@shiep.edu.cn

中图分类号:

TM614

基金项目:

国家重点研发计划(2021YFC3101602);华能集团总部科技项目(HNKJ20?H66)


Short‑time prediction of long‑distance offshore wind power based on ramp characteristics and improved PRAA
Author:
Affiliation:

(1.College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China; 2.Zhangjiakou Power Supply Company, State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075000, China; 3.College of Information,Shanghai Ocean University, Shanghai 201306, China; 4.Clean Energy Branch of Huaneng (Zhejiang) Energy Development Co., Ltd.,Hangzhou 310014, China)

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    摘要:

    深远海海域情况复杂,海面风速极易受海洋中尺度事件影响。所造成的异常数据点和Bump事件将导致爬坡检测准确率下降,影响深远海风电功率短期预测精度。因此,提出了一种同时考虑爬坡事件以及深远海气象因素的深远海风电功率短期预测方法。首先,设计基于状态标记和滑动窗口改进的参数和分辨率自适应算法(parameter and resolution adaptive algorithm,PRAA)实现爬坡事件检测并完成特征量提取;其次,分析深远海风速、风向及温度等多因素关联关系,扩充深远海气象因素特征样本维度,并通过主成分分析法(principal component analysis,PCA)深度挖掘潜在特征;最后,基于某海上风电场的实测数据,采用考虑爬坡和深远海气象因素的轻量梯度提升机(light gradient boosting machine,LightGBM)算法完成深远海风电功率的短期预测,仿真结果验证了所提方法的有效性。

    Abstract:

    The conditions in long-distance offshore areas are complex, and surface wind speeds are highly susceptible to the influence of mesoscale oceanic events. The resulting anomalous data points and bump events will decrease the accuracy of ramp-up detection, affecting the short-term forecasting precision of offshore wind power in long-distance sea areas. Therefore, a short-term forecasting method for offshore wind power in long-distance sea areas is proposed, which simultaneously considers ramp-up events and long-distance sea meteorological factors. Firstly, an improved parameter and resolution adaptive algorithm (PRAA) based on state marker and sliding window is designed to detect ramp-up events and extract features. Secondly, the correlation of multiple factors such as wind speed, wind direction and temperature in the long-distance offshore is analyzed to expand the dimension of the feature samples of the meteorological factors, and the potential features are deeply explored by principal component analysis (PCA). Finally, based on the measured data of a domestic offshore wind farm, the light gradient boosting machine (LightGBM) considering ramp-up and meteorological factors in long-distance sea areas is used to complete the short-term prediction of long-distance offshore wind power. Simulation results verify the effectiveness of the proposed method.

    参考文献
    [1] 祁和生.深远海域风电技术——海上风电新的制高点[J].太阳能,2018(6):5. QI Hesheng.Deep sea wind power technology is the new commanding point of offshore wind power[J].Solar Energy,2018(6):5.
    [2] 孙丽平,易晓亮,宋子恒.我国海上风电发展面临的挑战和相关建议[J].中外能源,2022,27(11):30-35. SUN Liping,YI Xiaoliang,SONG Ziheng.Challenges and suggestions for development of offshore wind power in China[J].Sino-Global Energy,2022,27(11):30-35.
    [3] 王帅,任军辉,娄彦涛,等.±525 kV/2 GW海上风电柔性直流送出系统海上换流站设备选型探讨[J].高压电器,2022,58(7):11-17. WANG Shuai,REN Junhui,LOU Yantao,et al.Discussion on equipment selection of offshore converter station of ±525 kV/2 GW offshore wind power flexible DC transmission system[J].High Voltage Apparatus,2022,58(7):11-17.
    [4] 李战龙,王祥君,王海云,等.基于直流风电机组的风电全直流输电系统综述[J].电测与仪表,2022,59(12):14-24. LI Zhanlong,WANG Xiangjun,WANG Haiyun,et al.Review of wind power ALL-DC transmission system based on DC wind turbine[J].Electrical Measurement & Instrumentation,2022,59(12):14-24.
    [5] 俞露杰,付子玉,朱介北,等.远海风电DRU-HVDC送出系统构网控制与启动方法综述[J].电力系统自动化,2023,47(24):63-79. YU Lujie,FU Ziyu,ZHU Jiebei,et al.Review on grid-forming control and start-up method of diode-rectifier-unit based HVDC transmission system for remote offshore wind farm[J].Automation of Electric Power Systems,2023,47(24):63-79.
    [6] 符杨,郑紫宸,时帅,等.考虑气象相似性与数值天气预报修正的海上风功率预测[J].电网技术,2019,43(4):1253-1260. FU Yang,ZHENG Zichen,SHI Shuai,et al.Offshore wind power forecasting considering meteorological similarity and NWP correction[J].Power System Technology,2019,43(4):1253-1260.
    [7] 高晨,赵勇,汪德良,等.海上风电机组电气设备状态检修技术研究现状与展望[J].电工技术学报,2022,37(S1):30-42. GAO Chen,ZHAO Yong,WANG Deliang,et al.Research status and prospect of condition based maintenance technology for offshore wind turbine electrical equipment[J].Transactions of China Electrotechnical Society,2022,37(S1):30-42.
    [8] 李国庆,徐亚男,江守其,等.海上风电经柔性直流联网系统受端交流故障穿越协调控制策略[J].电力系统保护与控制,2022,50(7):111-119. LI Guoqing,XU Yanan,JIANG Shouqi,et al.Coordinated control strategy for receiving-end AC fault ride-through of an MMC-HVDC connecting offshore wind power[J].Power System Protection and Control,2022,50(7):111-119.
    [9] 高小童,秦志龙,高新宇.含海上风电—光伏—储能的多能源发输电系统可靠性评估[J].发电技术,2022,43(4):626-635. GAO Xiaotong,QIN Zhilong,GAO Xinyu.Reliability evaluation of multi-energy generation and transmission system with offshore wind power-photovoltaic-energy storage[J].Power Generation Technology,2022,43(4):626-635.
    [10] 陈鸿琳,刘新苗,余浩,等.基于近似动态规划的海上风电制氢微网实时能量管理策略[J].电力建设,2022,43(12):94-102. CHEN Honglin,LIU Xinmiao,YU Hao,et al.Real-time energy management strategy based on approximate dynamic programming for offshore wind power-to-hydrogen microgrid[J].Electric Power Construction,2022,43(12):94-102.
    [11] 宋伟业,刘灵玥,阎洁,等.基于深度强化学习的海上风电集群自进化功率平滑控制方法[J].中国电力,2023,56(3):36-46. SONG Weiye,LIU Lingyue,YAN Jie,et al.Self-evolving power smooth control method for offshore wind power cluster based on deep reinforcement learning[J].Electric Power,2023,56(3):36-46.
    [12] 张东英,代悦,张旭,等.风电爬坡事件研究综述及展望[J].电网技术,2018,42(6):1783-1792. ZHANG Dongying,DAI Yue,ZHANG Xu,et al.Review and prospect of research on wind power ramp events[J].Power System Technology,2018,42(6):1783-1792.
    [13] 崔明建,孙元章,柯德平,等.考虑电网侧频率偏差的风电功率爬坡事件预测方法[J].电力系统自动化,2014,38(5):8-13. CUI Mingjian,SUN Yuanzhang,KE Deping,et al.Prediction method for wind power ramp events considering frequency deviation of power grid side[J].Automation of Electric Power Systems,2014,38(5):8-13.
    [14] OUYANG T H,HUANG H M,HE Y S.Ramp events forecasting based on long-term wind power prediction and correction[J].IET Renewable Power Generation,2019,13(15):2793-2801.
    [15] XIONG Y,ZHA X M,QIN L,et al.Research on wind power ramp events prediction based on strongly convective weather classification[J].IET Renewable Power Generation,2017,11(8):1278-1285.
    [16] DONG C,HUANG G G,CHENG G H.Offshore wind can power Canada[J].Energy,2021,236:121422.
    [17] 王鑫,李慧,叶林,等.考虑风速波动特性的VMD-GRU短期风电功率预测[J].电力科学与技术学报,2021,36(4):20-28. WANG Xin,LI Hui,YE Lin,et al.VMD-GRU based short-term wind power forecast considering wind speed fluctuation characteristics[J].Journal of Electric Power Science and Technology,2021,36(4):20-28.
    [18] 张帅可,罗萍萍.基于混合分布模型的风电功率超短期预测误差分析[J].电力科学与技术学报,2020,35(5):111-118. ZHANG Shuaike,LUO Pingping.Ultra short-time prediction error analysis of wind power based on mixed distribution model[J].Journal of Electric Power Science and Technology,2020,35(5):111-118.
    [19] 张爱枫,段新宇,何枭峰.基于CNN和LightGBM的新型风电功率预测模型[J].电测与仪表,2021,58(11):121-127. ZHANG Aifeng,DUAN Xinyu,HE Xiaofeng.A new wind power prediction model based on CNN and LightGBM[J].Electrical Measurement & Instrumentation,2021,58(11):121-127.
    [20] 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 Guangzheng,LU Liu,TANG Bo,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.
    [21] 唐振浩,孟庆煜,曹生现,等.基于小波深度置信网络的风电爬坡预测方法[J].太阳能学报,2019,40(11):3213-3220. TANG Zhenhao,MENG Qingyu,CAO Shengxian,et al.Wind power ramp prediction algorithm based on wavelet deep belief network[J].Acta Energiae Solaris Sinica,2019,40(11):3213-3220.
    [22] 张颖超,宗阳,邓华,等.基于趋势特征的风电功率爬坡事件检测方法[J].电测与仪表,2020,57(18):122-127+132. ZHANG Yingchao,ZONG Yang,DENG Hua,et al.Wind power ramp event detection method based on trend feature[J].Electrical Measurement & Instrumentation,2020,57(18):122-127+132.
    [23] 乔妍,韩丽,李梦洁.基于爬坡特征和云模型的风电功率预测误差区间评估[J].电力系统自动化,2022,46(11):75-84. QIAO Yan,HAN Li,LI Mengjie.Interval estimation of wind power forecasting error based on ramp features and cloud model[J].Automation of Electric Power Systems,2022,46(11):75-84.
    [24] OUYANG T,ZHA X M,QIN L,et al.Optimisation of time window size for wind power ramps prediction[J].IET Renewable Power Generation,2017,11(8):1270-1277.
    [25] 屈尹鹏,徐箭,姜尚光,等.基于频繁模式挖掘的风电爬坡事件统计特性建模及预测[J].电力系统自动化,2021,45(1):36-43. QU Yinpeng,XU Jian,JIANG Shangguang,et al.Frequent pattern mining based modeling and forecasting for statistical characteristics of wind power ramp events[J].Automation of Electric Power Systems,2021,45(1):36-43.
    [26] QU Y P,XU J,SUN Y Z,et al.A parameter and resolution adaptive algorithm for rapid detection of ramp events in different timescale databases of the power system[J].International Journal of Electrical Power & Energy Systems,2019,112:393-403.
    [27] 熊予涵,彭小圣,杨子民,等.基于参数自适应旋转门和Bump事件筛选的风电爬坡事件识别[J].南方电网技术,2023,17(2):101-110. XIONG Yuhan,PENG Xiaosheng,YANG Zimin,et al.Identification of wind power ramp events based on parameter adaptive swinging door and bump event selection[J].Southern Power System Technology,2023,17(2):101-110.
    [28] 迟永宁,梁伟,张占奎,等.大规模海上风电输电与并网关键技术研究综述[J].中国电机工程学报,2016,36(14):3758-3771. CHI Yongning,LIANG Wei,ZHANG Zhankui,et al.An overview on key technologies regarding power transmission and grid integration of large scale offshore wind power[J].Proceedings of the CSEE,2016,36(14):3758-3771.
    [29] 熊音笛,刘开培,秦亮,等.基于时序数据动态天气划分的短期风电功率预测方法[J].电网技术,2019,43(9):3353-3359. XIONG Yindi,LIU Kaipei,QIN Liang,et al.Short-term wind power prediction method based on dynamic wind power weather division of time sequence data[J].Power System Technology,2019,43(9):3353-3359.
    [30] 李俊卿,李秋佳,石天宇,等.基于数据挖掘的风电功率预测特征选择方法[J].电测与仪表,2019,56(10):87-92. LI Junqing,LI Qiujia,SHI Tianyu,et al.Feature selection method for wind power prediction based on data mining[J].Electrical Measurement & Instrumentation,2019,56(10):87-92.
    [31] LANDBERG L.Short-term prediction of local wind conditions[J].Journal of Wind Engineering and Industrial Aerodynamics,2001,89(3/4):235-245.
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引用本文

黄冬梅,张佳慧,时 帅,等.基于爬坡特征与改进PRAA的深远海风电功率短期预测研究[J].电力科学与技术学报,2024,(3):187-198.
HUANG Dongmei, ZHANG Jiahui, SHI Shuai, et al. Short‑time prediction of long‑distance offshore wind power based on ramp characteristics and improved PRAA[J]. Journal of Electric Power Science and Technology,2024,(3):187-198.

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  • 在线发布日期: 2024-07-25
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