Optimal wind power prediction based on local ramp error correction of wind speed

被引：0
|
作者：
Xiao Y. [1 ,2 ]
Li C. [3 ]
Liu R. [1 ,2 ]
Zuo J. [4 ]
Li Y. [1 ,2 ]
机构：
[1] State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan
[2] Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan
[3] Changjiang Institute of Survey, Planning, Design and Research, Wuhan
[4] Guangdong Electric Power Dispatch Center, Guangzhou
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2019年 / 39卷 / 03期
关键词：
Grey wolf optimization; Lagging quality; Least square support vector machine; Local ramp error correction; Predicted wind speed; Wind power prediction;
D O I：
10.16081/j.issn.1006-6047.2019.03.029
中图分类号：
学科分类号：
摘要：
Accurate wind power prediction is significant for secure and stable operation of power system, and the lag is the main reason of wind power prediction error, especially when wind speed changes rapidly, the lag will result in big error. Considering strong relationship between wind speed and wind power, an error correction method based on LR(Local Ramp) of wind speed is proposed to improve the lag, and the predicted wind speed after correction and historical wind power are taken as input for wind power prediction. The parameters of LSSVM(Least Square Support Vector Machine) are optimized by using GWO(Grey Wolf Optimization) algorithm to improve the accuracy of wind power prediction. Case results show that the proposed method can effectively improve the accuracy of wind power prediction. © 2019, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码：182 / 188
页数：6
相关论文
共 17 条
  • [1] Xue Y., Yu C., Zhao J., Et al., A review on shortterm and ultra-short-term wind power prediction, Automation of Electric Power Systems, 39, 6, pp. 141-151, (2015)
  • [2] Xiao Q., Li W., Li Z., Et al., Wind speed and power prediction based on improved wavelet-BP neural network, Power System Protection and Control, 42, 15, pp. 80-86, (2014)
  • [3] Dong L., Wang L., Gao S., Et al., Modeling and analysis of prediction of wind power generation in the large wind farm based on chaotic time series, Transactions of China Electrotechnical Society, 23, 12, pp. 125-129, (2008)
  • [4] Yang R., Lin W., Tsai M., Et al., Particle swarm optimisation aided least-square support vector machine for load forecast with spikes, IET Generation, Transmission & Distribution, 10, 5, pp. 1145-1153, (2016)
  • [5] Ouyang T., Zha X., Qin L., Et al., Short-term wind power prediction based on kernel function switching, Electric Power Automation Equipment, 36, 9, pp. 80-86, (2016)
  • [6] Cheng Q., Xu L., Cheng Y., Et al., Short-term wind power forecasting method based on EEMD and LS-SVM model, Electric Power Automation Equipment, 38, 5, pp. 27-35, (2018)
  • [7] Mao M., Zhou S., Su J., Short-term wind power forecast based on ridgelet neural network, Automation of Electric Power Systems, 35, 7, pp. 70-74, (2011)
  • [8] Wang L., Dong L., Liao X., Et al., Short-term power pre-diction of a wind farm based on wavelet analysis, Proceedings of the CSEE, 28, 29, pp. 30-33, (2008)
  • [9] Yang X., Sun B., Zhang X., Et al., Short-term wind speed forecasting based on support vector machine with similar data, Proceedings of the CSEE, 32, 4, pp. 35-41, (2012)
  • [10] Mao M., Cao Y., Zhou S., Improved short-term wind power forecasting method based on accumulative error correction, Automation of Electric Power Systems, 37, 23, pp. 34-38, (2013)
12