Interval Estimation of Wind Power Forecasting Error Based on Ramp Features and Cloud Model

被引：0
|
作者：
Qiao Y. [1 ]
Han L. [1 ]
Li M. [1 ]
机构：
[1] School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Cloud model; Error classification; Forecasting error; Improved swinging door algorithm; Interval estimation of wind power; Ramp feature; Wind power;
D O I：
10.7500/AEPS20210603006
中图分类号：
学科分类号：
摘要：
In order to meet the higher and higher reliability requirements of the forecasting error interval estimation results for optimal operation of the power system, and improve the poor adaptability of traditional methods when small-probability wind power ramp events occur, a interval estimation method for forecasting error of wind power based on the classification of ramp features and cloud model is proposed. Models for each type of data are built to improve the applicability of the estimation method with different ramp types. First, the improved swinging door algorithm is used to identify ramp and ramp features are obtained. The forecasting errors are classified according to the ramp features. The cloud models for up-ramp error and down-ramp error are established separately, and the K-means algorithm is used for non-ramp error to obtain the interval ranges corresponding to different forecasting error types. Then, the wind power and ramp features data are used as input of the model, and the forecasting error types are used as output. The estimation model is established to obtain the forecasting error estimation interval of wind power. Finally, the wind power data from the Elia website is utilized to conduct an example analysis, and the results show that the proposed method has better estimation effect of wind power error interval. © 2022 Automation of Electric Power Systems Press.
引用
收藏
页码：75 / 84
页数：9
相关论文
共 28 条
  • [1] DREW D R, CANNON D J, BARLOW J F, Et al., The importance of forecasting regional wind power ramping: a case study for the UK, Renewable Energy, 114, pp. 1201-1208, (2017)
  • [2] LI Jinghua, SANG Chuanchuan, GAN Yifu, Et al., A review of researches on wind power forecasting technology, Modern Electric Power, 34, 3, pp. 1-11, (2017)
  • [3] RAJAGOPALAN S, SANTOSO S., Wind power forecasting and error analysis using the autoregressive moving average modeling, 2009 IEEE Power & Energy Society General Meeting, (2009)
  • [4] XUE Yang, WANG Lin, WANG Shu, Et al., An ultra-short-term wind power forecasting model combined with CNN and GRU networks, Renewable Energy Resources, 37, 3, pp. 456-462, (2019)
  • [5] YUE Xiaoyu, PENG Xiangang, LIN Li, Short-term wind power forecasting based on whales optimization algorithm and support vector machine, Proceedings of the CSU-EPSA, 32, 2, pp. 146-150, (2020)
  • [6] WU Wenzu, QIAO Ying, LU Zongxiang, Et al., Methods and prospects for probabilistic forecasting of wind power, Automation of Electric Power Systems, 41, 18, pp. 167-175, (2017)
  • [7] YANG Hong, YAN Yujie, WANG Yu, Applicability of Beta distribution on wind power forecast error modeling, Electrical Measurement & Instrumentation, 57, 11, pp. 37-41, (2020)
  • [8] ZHANG G, LI Z X, ZHANG K S, Et al., Multi-objective interval prediction of wind power based on conditional Copula function, Journal of Modern Power Systems and Clean Energy, 7, 4, pp. 802-812, (2019)
  • [9] MEN Z X, YEE E, LIEN F S, Et al., Short-term wind speed and power forecasting using an ensemble of mixture density neural networks, Renewable Energy, 87, pp. 203-211, (2016)
  • [10] WANG Zhao, WANG Bo, FENG Shuanglei, Et al., Probabilistic forecasts based on quantile regression for regional wind farms, Power System Technology, 44, 4, pp. 1368-1375, (2020)
123