Ultra-short-term wind speed prediction based on an adaptive integrated model

被引：0
|
作者：
Guan Y. [1 ,2 ]
Yu M. [1 ,2 ]
Hu J. [1 ,2 ]
机构：
[1] Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan
[2] College of Science, Wuhan University of Science and Technology, Wuhan
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2022年 / 50卷 / 04期
基金：
中国国家自然科学基金;
关键词：
ARIMA; ELM; Parameter optimized variational modal decomposition; PSO; Ultra-short-term wind speed prediction;
D O I：
10.19783/j.cnki.pspc.210446
中图分类号：
学科分类号：
摘要：
Wind speed prediction has a significant impact on the stable and safe operation of a power system. According to the intermittent and random nature of wind speed, an integrated model of variational modal decomposition (VMD) based on grid search optimization algorithm (GS) and PSO-ELM is proposed for ultra-short-term wind speed prediction. First, the VMD is used to decompose wind speed sequence into a series of sub-sequences. By taking the orthogonality as the fitness function, the GS is used to search the key parameters of VMD adaptively, including the number of decomposed layers and a penalty factor. The purpose is to ensure information orthogonality between each component and to suppress coupling components. Then, the extreme learning machine (ELM) method is used to predict each sub-sequence. Given the unstable prediction of this model, particle swarm algorithm (PSO) is used to optimize the parameters of the initial weight and threshold, and the input dimension of each sub-sequence is determined adaptively by using the auto-regressive integrated moving average model (ARIMA). Finally, the predicted results of each sub-sequence are superimposed to obtain the final predicted wind speed. The result shows that the proposed integrated model is remarkably superior to all considered benchmark models in prediction accuracy. © 2022 Power System Protection and Control Press.
引用
收藏
页码：120 / 128
页数：8
相关论文
共 33 条
  • [1] SU Bohao, TANG Yong, YE Lin, Et al., Integrated frequency control strategy for wind power cluster with multiple temporal-spatial scale coordination based on H-DMPC, Proceedings of the CSEE, 39, 1, pp. 155-167, (2019)
  • [2] YU Haifeng, PAN Liqiang, WU Yaru, Et al., Analysis of energy storage improving the reliability of power system with high proportional wind power, Power System and Clean Energy, 36, 6, pp. 92-98, (2020)
  • [3] LI Jiongcong, SHI Jiahao, WU Qing, Et al., Vulnerability analysis and quantitative evaluation of the power system containing wind power, Power System and Clean Energy, 36, 1, pp. 46-54, (2020)
  • [4] JUNG C, TAUBERT D, SCHINDLER D., The temporal variability of global wind energy long-term trends and inter-annual variability, Energy Conversion and Management, 188, 5, pp. 462-472, (2019)
  • [5] QIAN Z, PEI Y, ZAREIPOUR H, Et al., A review and discussion of decomposition-based hybrid models for wind energy forecasting applications, Applied Energy, 235, 2, pp. 939-953, (2019)
  • [6] CHEN Jinfu, ZHU Qiaomu, SHI Dongyuan, Et al., A multi-step wind speed prediction model for multiple sites leveraging spatio-temporal correlation, Proceedings of the CSEE, 39, 7, pp. 2093-2106, (2019)
  • [7] WANG Xin, LI Hui, YE Lin, Et al., VMD-GRU based shortterm wind power forecast considering wind speed fluctuation characteristics, Journal of Electric Power Science and Technology, 36, 4, pp. 20-28, (2021)
  • [8] ZHANG Shuaike, LUO Pingping, Ultra short-time prediction error analysis of wind power based on mixed distribution model, Journal of Electric Power Science and Technology, 35, 5, pp. 111-118, (2020)
  • [9] JUNG J, BROADWATERR P., Current status and future advances for wind speed and power forecasting, Renewable & Sustainable Energy Reviews, 31, 3, pp. 762-777, (2014)
  • [10] WANG Jun, LI Xia, ZHOU Xidong, Et al., Ultra-short term wind speed prediction based on VMD and LSTM, Power System Protection and Control, 48, 11, pp. 45-52, (2020)
1234