基于信息粒化和支持向量机的风电场功率变化趋势和变化空间的预测算法

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (898 KB) HTML (0 KB)
Export: BibTeX | EndNote (RIS)

Abstract It is significant to predict the change trend and change space of the wind power accurately for wind farm and the power system stable operation as wind power has the characteristics of volatility, intermittent and randomness. This paper conducted the time series of daily active power with fuzzy information granulation, and did the regression forecasting of the data after granulation with the support vector machine(SVM) prediction model, then simulated and tested this algorithm using the data from the wind farm located in the Gansu Jiu Quan wind farm of China. The results show that the new method has better prediction about the change trend and change space of the future wind power, and prove the feasibility and effectiveness of the model.

Key words： keywords：Wind power generation information granulation support vector machine power prediction

Received: 11 September 2012 Published: 12 December 2013

PACS:

TM614

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Chen Wei
	Zhao Qingtang
	Zhao Jinping

Cite this article:

Chen Wei,Zhao Qingtang,Zhao Jinping. Combined Model Based on the Information Granulation and SVM for Predicting Change Trend and Change Space of Wind Power[J]. Transactions of China Electrotechnical Society, 2013, 28(1增): 169-173.

URL:

http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2013/V28/I1增/169

[1] 刘永前, 韩爽, 胡永生. 风电场出力短期预报研究综述[J]. 现代电力, 2007, 24(5): 6-11.
Liu Yongqian, Han Shuang, Hu Yongsheng. Review on short-term wind power prediction[J]. Modern Electric Power, 2007, 24(5): 6-11.
[2] Sideratos G, Hatziargyriou N D. An advanced statistical method for wind power forecasting[J]. IEEE Transactions on Power Systems, 2007, 22(1): 258-265.
[3] 丁明, 张立军, 吴义纯. 基于时间序列分析的风电场风速预测模型[J]. 电力自动化设备, 2005, 25(8): 32-34.
Ding Ming, Zhang Lijun, Wu Yichun. Wind speed forecast model for wind farms based on time series analysis[J]. Electric Power Automation Equipment, 2005, 25(8): 32-34.
[4] 李明干, 孙建利, 刘沛. 基于卡尔曼滤波的电力系统短期负荷预测[J]. 继电器, 2004, 32(4): 9-12.
Li Minggan, Sun Jianli, Liu Pei. Short-term load foreccastof power system based on Kalman filter[J]. Relay, 2004, 32(4): 9-12.
[5] 戚双斌, 王维庆, 张新燕. 基于支持向量机的风速与风功率预测方法研究[J]. 华东电力, 2009, 37(9):
[6] 牛晨光, 刘丛. 基于相空间重构的神经网络的神经网络短期风电预测模型[J]. 中国电力, 2011, 44(11): 73-77.
Niu Chenguang, Liu Cong. Neural network model for short-term wind power prediction based on phases pace reconstruction[J]. Electric Power, 44(11): 73-77.
[7] 范高峰, 王伟胜, 刘纯. 基于人工神经网络的风电功率短期预测系统[J]. 电网技术, 2008, 32(22): 72-76.
Fang Gaofeng, Wang Weisheng, Liu Chun. Artificial neural network based wind power short term prediction system[J]. Power system Techndogy, 2008, 32(22): 72-76.
[8] 李鸿. 粒集理论: 粒计算的新模型[J]. 重庆邮电大学学报(自然科学版), 2007, 19(4): 397-404.
Li Hong. Theory of granular set: A new model ofgranular computing[J]. Journal of Chongqing University of Posts and Telecommunications( Natural Science), 2007,19(4): 397-404.
[9] 黄兆华, 邓毅雄. 粒计算及其应用的研究[J]. 华东交通大学学报, 2005, 22(5): 124-127.
Huang Zhaohua, Deng Yixiong. An approach for granular computing and its application[J]. Journal of East China Jiaotong University, 2005, 22(5): 124-127.
[10] Vapnik V. The nature of statistical learning[M]. New York: Springer, 1995.
[11] Witold Pedrycz. Knowledge-based clustering from data information granules[M]. John Wile Y&Sons. Inc, 2005.
[12] A Bargiela, W Pedrycz. Granular computing: an introduction[M]. Kluwer Academic Publishers, Dodrection, 2003.
[13] 李洋. 基于信息粒化的机器学习分类及回归预测分析[D]. 北京: 北京师范大学, 2009.