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基于时空神经网络的风电场超短期风速预测模型
作者:
作者单位:

1.清华大学电机工程与应用电子技术系,北京市 100084;2.电力系统及大型发电设备安全控制和仿真国家重点实验室,清华大学,北京市 100084;3.清华大学电子工程系,北京市 100084

摘要:

随着风电场的大规模接入,提高风电场风速的预测精度对于促进可再生能源的消纳具有重大意义。传统的预测方法通常根据风电场单一高度的历史风速进行预测,当预测的时间尺度达到三四小时的时候,预测误差较大。不同高度的风速、风向数据蕴含了风电场内部的时空相关性,数值天气预报数据也体现了风电场周边的大气运动对风速发展规律的影响。文中在输入数据层面,同时引入了不同高度的风速、风向数据和数值天气预报数据。为了充分挖掘数据中的规律,提出了一种新的时空神经网络,采用深度卷积神经网络和双向门控循环单元,分别提取风速、风向等历史数据以及数值天气预报的时空特征,并利用融合后的特征进行风速预测。最后,利用中国东北某风电场的实际测量数据,验证了算法的有效性。

关键词:

基金项目:

国家重点研发计划资助项目(2018YFB0904200);国家电网有限公司科技项目(SGLNDKOOKJJS1800266)。

通信作者:

作者简介:

凡航(1993—),男,博士研究生,主要研究方向:人工智能技术在电力系统中的应用、可再生能源预测。E-mail:fanhang123456@163.com
张雪敏(1979—),女,通信作者,博士,副教授,主要研究方向:电力系统稳定控制与连锁故障、可再生能源预测。E-mail:zhangxuemin@mail.tsinghua.edu.cn
梅生伟(1964—),男,博士,教授,主要研究方向:电力系统分析、储能、可再生能源发电、能源互联网。E-mail:meishengwei@mail.tsinghua.edu.cn


Ultra-short-term Wind Speed Prediction Model for Wind Farms Based on Spatiotemporal Neural Network
Author:
Affiliation:

1.Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;2.State Key Laboratory of Power System and Generation Equipment, Tsinghua University, Beijing 100084, China;3.Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Abstract:

With the large-scale integration of wind farms, improving the prediction accuracy of wind speed in wind farms is of great significance to promote the consumption of renewable energy. Traditional prediction methods are usually based on the historical wind speed of a single altitude in the wind farm. When the prediction horizon reaches about three or four hours, the prediction error becomes relatively large. Wind speed and direction data at different altitudes contain the spatiotemporal correlation and the numerical weather prediction data reflects the influence of atmospheric motion around the wind farm on the variation of wind speed. In this paper, wind speed and direction data at different altitudes and numerical weather prediction data are introduced at the input data level. In order to fully exploit the rules of data, a new spatiotemporal neural network (STNN) is proposed. The deep convolutional network and the bidirectional gated recurrent unit are used to extract the spatiotemporal features of historical wind speed, wind direction and numerical weather prediction, respectively. The fused features are used to predict the wind speed. Finally, the actual measurement data of a wind farm in northeast China is used to verify the effectiveness of the algorithm.

Keywords:

Foundation:
This work is supported by National Key R&D Program of China (No. 2018YFB0904200) and State Grid Corporation of China (No. SGLNDKOOKJJS1800266).
引用本文
[1]凡航,张雪敏,梅生伟,等.基于时空神经网络的风电场超短期风速预测模型[J].电力系统自动化,2021,45(1):28-35. DOI:10.7500/AEPS20190831001.
FAN Hang, ZHANG Xuemin, MEI Shengwei, et al. Ultra-short-term Wind Speed Prediction Model for Wind Farms Based on Spatiotemporal Neural Network[J]. Automation of Electric Power Systems, 2021, 45(1):28-35. DOI:10.7500/AEPS20190831001.
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  • 收稿日期:2019-08-31
  • 最后修改日期:2019-11-16
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  • 在线发布日期: 2021-01-05
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