Wind Power Prediction Based on Wavelet-Time Series Combined Model

doi:10.3969/j.issn.1000-7229.2016.01.019

Abstract

Abstract:

With the expansion of the scale of wind power, the timely and accurate prediction of wind power output is of great significance. However, due to the uncertainty of wind speed, the wind power is difficult to control. Through the trend analysis of wind speed and power, we establish a mathematical model of wind power based on wind speed. Then, taking the wind speed forecasting as a breakthrough point, the irregular history of wind speed is pattern decomposed based on wavelet decomposition mode. We analyzed the decomposed historical series, adopted the appropriate forecasting model to forecast respectively, reduced the series to the original series to obtained the predicted wind speed, finally obtained the predicted wind power through calculation. Through the calculation at a certain place, the accuracy and reliability of wind power forecasting with using wavelet decomposition and time series model are proved.

Key words: wind speed, wind power, wavelet analysis, time series

CLC Number:

TM 614

QIU Jinpeng, NIU Dongxiao. Wind Power Prediction Based on Wavelet-Time Series Combined Model[J]. Electric Power Construction, 2016, 37(1): 125-130.

References

［1］佚名.浅谈风电功率预测系统的必要性［EB/OL］. http://b2b.bjx.com.cn/8501357/b2bnews-517750.html.
Anonymous. Introduction to the necessity of wind power prediction system［EB/OL］. http://b2b.bjx.com.cn/8501357/b2bnews-517750.html.
［2］孙翰墨. 基于ARMA模型的风电机组风速预测研究［D］. 北京：华北电力大学,2011.
SUN Hanmo. The research on wind speed prediction for WTG based on ARMA model［D］. Beijing: North China Electric Power University,2011,
［3］苏鹏宇. 考虑风速变化模式的风速预报方法研究［D］. 哈尔滨: 哈尔滨工业大学,2013
SU Pengyu. Study of pattern-based wind speed porediction［D］. Harbin： Harbin Institute of Technology,2013.
［4］杨明,范澍,韩学山,等. 基于分量稀疏贝叶斯学习的风电场输出功率概率预测方法［J］.电力系统自动化,2012,36(14):125-130.
YANG Ming, FAN Shu, HAN Xueshan,et al. Wind farm generation forecast based on componential sparse Bayesian learning［J］. Automation of Electric Power Systems, 2012,36(14):125-130.
［5］王彩霞,鲁宗相,乔颖,等. 基于非参数回归模型的短期风电功率预测［J］. 电力系统自动化,2010,34(16):78-83.
WANG Caixia, LU Zongxiang, QIAO Ying, et al. Short-term wind power forecast based on non-parametric regression model［J］. Automation of Electric Power Systems, 2010,34(16):78-83.
［6］李智,韩学山,韩力,等. 地区电网风电场功率超短期预测方法［J］. 电力系统自动化. 2010,34(7):90-94.
LI Zhi, HAN Xueshan, HAN Li, et al. An ultra-short-term wind power forecasting method in regional grids［J］. Automation of Electric Power Systems, 2010,34(7):90-94.
［7］王丽婕,冬雷,廖晓钟,等. 基于小波分析的风电场短期发电功率预测［J］.中国电机工程学报.2009,29(28):30-33.
WANG Lijie, DONG Lei, LIAO Xiaozhong, et al. Short-term power prediction of a wind farm based on wavelet analysis［J］. Proceedings of the CSEE, 2009,29(28):30-33.
［8］杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究［J］.中国电机工程学报,2005,25(11):1-5.
YANG Xiuyuan, XIAO Yang, CHEN Shuyong. Wind speed and generated power forecasting in wind farm［J］. Proceedings of the CSEE, 2005,25(11):1-5.
［9］邰能灵,候志俭,李涛.基于小波分析的电力系统短期负荷预测方法［J］.中国电机工程学报,2003,23(1):45-50.
TAI Nengling, HOU Zhijian, LI Tao. New principle based on wavelet transform for power system short-term load forecasting［J］. Proceedings of the CSEE, 2003,23(1):45-50.
［10］师洪涛, 杨静玲, 丁茂生, 等. 基于小波-BP 神经网络的短期风电功率预测方法［J］. 电力系统自动化, 2011,35(16): 44-48.
SHI Hongtao, YANG Jingling, DING Maosheng, et al. A short-term wind power prediction method based on wavelet decomposition and BP neural network［J］. Automation of Electric Power Systems, 2011,35(16): 44-48.
［11］温锦斌,王昕,李立学,等.基于频域分解的短期风电负荷预测［J］. 电工技术学报, 2013, 28(5): 66-72.
Wen Jinbin, Wang Xin, Li Lixue, et al. Short-term wind power load forecasting based on frequency domain decomposition［J］. Transactions of China Electrotechnical Society, 2013, 28(5): 66-72.
［12］蔡凯,谭伦农,李春林.时间序列与神经网络法相结合的短期风速预测［J］.电网技术,2008,32(8):82-85.
CAI Kai, TAN Lunnong, LI Chunlin. Short-term wind speed forecasting combing time series and neural network method［J］. Power System Technology,2008,32(8):82-85.
［13］范高锋,王伟胜,刘纯.基于人工神经网络的风电功率预测［J］.中国电机工程学报，2008,28(34):118-123.
FAN Gaofeng, WANG Weisheng, LIU Chun. Wind power prediction based on artificial neural network［J］. Proceedings of the CSEE，2008,28(34):118-123.
［14］ALEXANDRE C, ANTONIO C, JORGE N. A review on the young history of the wind Power short-term Prediction［J］. Renewable and Sustainable Energy Reviews, 2008,12(6):1725-1744.
［15］王丽婕, 廖晓钟, 高阳, 等. 风电场发电功率的建模和预测研究综述［J］. 电力系统保护与控制, 2009, 37(13):118-121.
WANG Lijie, LIAO Xiaozhong, GAO Yang, et al. Summarization of modeling and prediction of wind power generation［J］. Power System Protection and Control, 2009, 37(13):118-121.
［16］刘刚. 风电场风速及功率预测系统研究［D］. 南昌：华东交通大学, 2011.
LIU Gang. Researches on wind speed and wind power forecasting system for wind farm［D］. Nanchang: East China Jiao Tong University, 2011.

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