MODELING METHOD OF WIND SPEED RANDOMNESS BASED ON COMPONENT DEPENDENCE

被引:0
|
作者
Zhang J. [1 ,2 ]
Wang J. [3 ]
Liu H. [4 ]
Wu L. [4 ]
Wang X. [5 ]
机构
[1] State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin
[2] School of Electrical Engineering, Hebei University of Technology, Tianjin
[3] College of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin
[4] Electric Power Research Institute of State Grid Hebei Electric Power Co.,Ltd., Beijing
[5] Hebei Branch, China Huaneng Group Co.,Ltd., Shijiazhuang
来源
关键词
component dependency; feature extraction; randomness; statistical method; wind speed;
D O I
10.19912/j.0254-0096.tynxb.2022-1636
中图分类号
学科分类号
摘要
Due to the influence of microtopography and micrometeorology,the randomness characteristics of wind speed in wind farms are complex. To accurately describe the randomness characteristics of wind speed,a feature modeling method based on component dependency is proposed. Firstly,the randomness of the wind speed series is extracted,and the wind speed is decomposed into several modal components with different frequencies by using the variational mode decomposition (VMD). With the serial autocorrelation coefficient(AC)as the index,the wind speed component is divided,and the fluctuation component and randomness component of wind speed are obtained. Then,considering the dependence of the wind speed randomness component on the fluctuation component,the normal distribution is used to describe the randomness feature under different wind speeds,and a wind speed randomness model based on component dependency is established. The effectiveness of this method is verified by the operation data of a wind farm in Zhangjiakou,North China. The experimental results show that this method can better reproduce the random characteristics of wind speed series. © 2024 Science Press. All rights reserved.
引用
收藏
页码:109 / 115
页数:6
相关论文
共 16 条
  • [1] YU D R, WAN J., Uncertainty modeling theory of wind power [M], (2017)
  • [2] ZHANG Y G, Et al., A hybrid forecasting system with complexity identification and improved optimization for short-term wind speed prediction [J], Energy conversion and management, 270, (2022)
  • [3] ZHANG N, HUANG Y H, WANG J,, Et al., Analysis of wind power fluctuation characteristics of three- north regions based on clustering algorithm [J], Electrical measurement & instrumentation, 57, 6, pp. 73-81, (2020)
  • [4] WANG J J, ZHUANG Z Z,, Et al., An optimized complementary prediction method based on data feature extraction for wind speed forecasting [J], Sustainable energy technologies and assessments, 52, (2022)
  • [5] WANG J Y., A combined model based on data preprocessing strategy and multi- objective optimization algorithm for short- term wind speed forecasting [J], Applied energy, 241, pp. 519-539, (2019)
  • [6] YANG S, LI X F,, Et al., Wind power fluctuation characteristics of Three North regions based on clustering algorithm[J], The journal of engineering, 2017, 13, pp. 2266-2270, (2017)
  • [7] XIAO Z G,, XIA X, Et al., A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting[J], Applied energy, 215, pp. 131-144, (2018)
  • [8] ZHANG Y, Et al., A hybrid energy storage system with optimized operating strategy for mitigating wind power fluctuations[J], Renewable energy, 125, pp. 121-132, (2018)
  • [9] LIU H, CAO Q., Wind speed prediction through neural network combination based on wavelet decomposition[J], Electrical automation, 43, 1, pp. 45-47, (2021)
  • [10] GAO Y, ZHONG H Y,, CHEN X Y,, Et al., Ultra short term wind speed forecasting based on neural network and wavelet analysis[J], Renewable energy resources, 34, 5, pp. 705-711, (2016)