一种风电功率混沌时间序列概率区间简易预测模型

章国勇; 伍永刚; 张洋; 代贤良

doi:10.7498/aps.63.138801

摘要

本文基于极限学习机构建了一种简易模型以直接输出风电功率概率区间. 同时，为优化模型训练过程中输出区间的性能，本文基于对数据集区间带偏差信息的分析构建了一种新的优化准则，并采用量子细菌觅食优化算法以获取问题的最优解，提高模型泛化能力. 对比分析两个风电场在不同置信水平和不同优化准则下的概率预测结果，仿真表明本文模型具有更高的可靠性和更窄的区间带宽，可为风电并网安全稳定运行提供决策支持.

关键词:

Integration of wind power into grids requires accurate forecasting, however, traditional wind power point forecast errors are unavoidable and they cannot be eliminated due to the highly volatile and uncertain in the chaotic time series of wind power. Unlike point prediction, which conveys no information about the prediction accuracy, probabilistic interval forecasts can provide a range, within which the target will lie with a certain probability, for estimating the potential impacts and risks facing the system operation. Most existing prediction interval (PI) construction methods are often placed after a deterministic forecasting model with or without prior assumptions, this paper propose a novel lower-upper bound estimation approach using extreme learning machine to directly construct PIs for wind power series. Based on the analysis of the interval forecasting error information in training dataset, a new problem formulation is developed in this method to get better PIs. In addition, in order to obtain the global optimal solution of the above model, a quantum bacterial foraging optimization algorithm is proposed by introducing the theory of quantum mechanics into bacteria foraging behavior. The testing results from two real wind farms with different confidence probability and optimization criterion demonstrate the excellent quality of PIs in terms of both reliability and sharpness, which provide a support for the steady operation of power system with wind power integration.

Keywords:

作者及机构信息

1.
华中科技大学水电与数字化工程学院, 武汉 430074;

2.
西澳大利亚大学电气电子及计算机学院, 澳大利亚珀斯 6009

基金项目: 国家自然科学基金（批准号：51379081）和湖北省自然科学基金（批准号：2011CDA032）资助的课题.

Authors and contacts

1.
College of Hydroelectricity and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;

2.
School of Electrical, electronic and computer engineering, University of Western Australia, WA 6009, Australia

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 51379081), and the Natural Science Foundation of Hubei Province, China (Grant Nos. 2011CDA032).

参考文献

[1]	Jung J, Broadwater R P 2014 Renewable and Sustainable Energy Reviews 31 762
[2]	Zhang X Q, Liang J 2012 Acta Phys. Sin. 61 190507 (in Chinese) [张学清, 梁军 2012 61 190507]
[3]	Zhang Y 2013 Chin. Phys. B 22 050502
[4]	Qu H, Ma W T, Zhao J H, Chen B D 2013 Chin. Phys. Lett. 30 110505
[5]	Zhang X Q, Liang J 2013 Acta Phys. Sin. 62 050505 (in Chinese) [张学清, 梁军 2013 62 050505]
[6]	Liu D W, Guo J B, Huang Y H, Wang W S 2013 Proceeding of the CSEE 33 9 (in Chinese) [刘德伟, 郭剑波, 黄越辉, 王伟胜 2013 中国电机工程学报 33 9]
[7]	Wu X S, Zhang B H, Yuan X M, Li G W, Luo G, Zhou Y 2013 Proceeding of the CSEE 33 45 (in Chinese) [吴小珊, 张步涵, 袁小明, 李高望, 罗钢, 周杨 2013 中国电机工程学报 33 45]
[8]	Zhang Z S, Sun Y Z, Gao D W, Lin J, Cheng L 2013 IEEE Trans. on Power Systems 28 3114
[9]	Jiang R W, Wang J H, Zhang M H, Guan Y P 2013 IEEE Trans. on Power Systems 28 2271
[10]	Lange M 2005 J. Sol. Energy Eng. 127 177
[11]	Zhou S L, Mao M Q, Shu J H 2011 Proceeding of the CSEE 31 10 (in Chinese)[周松林, 茆美琴, 苏建徽 2011 中国电机工程学报 31 10]
[12]	Li Z, Han X S, Yang M, Zhong S M 2011 Automation of Electric Power Systems 35 83 (in Chinese) [李智, 韩学山, 杨明, 钟世民 2011 电力系统自动化 35 83]
[13]	Sideratos G, Hatziargyriou N D 2012 IEEE Trans. Power Syst. 27 1788
[14]	Jeon J, Taylor J W 2012 J. Amer. Statist. Assoc. 107 66
[15]	Bessa R J, Miranda V, Botterud A, Zhou Z, Wang J 2012 Renew. Energy 40 29
[16]	Khosravi A, Nahavandi S, Creighton D, Atiya A 2011 IEEE Trans. Neural Networks 22 337
[17]	Huang G B, Zhu Q Y, Siew C K 2006 Neurocomputing 70 489
[18]	Wang X Y, Han M 2012 Acta Phys. Sin. 61 080507 (in Chinese)[王新迎, 韩敏 2012 61 080507]
[19]	Gao G Y, Jiang G P 2012 Acta Phys. Sin. 61 040506 (in Chinese)[高光勇, 梁国平 2012 61 040506]
[20]	Khosravi A, Nahavandi S 2013 IEEE Trans. Sustainable Energy 4 849
[21]	Quan H, Srinivasan D, Khosravi A 2014 IEEE Trans. Neural Netw. Learn. Syst. 25 303
[22]	Van den Bergh F, Engelbrecht A P 2002 Proceedings of the IEEE International Conf on Systems, Man and Cybernetics, October 6-9, 2002 p94
[23]	Sun J, Feng B, Xu W B 2004 Proceedings of the IEEE Congress on Evolutionary Computation, Dec. 1-3, 2004 p325
[24]	Passino K M 2002 IEEE Control Systems Magazine 22 52
[25]	Shrestha D L, Solomatine D P 2006 Neural Netw. 19 225
[26]	Fang W, Sun J, Xie Z P, Xu W B 2010 Acta Phys. Sin. 59 3686 (in Chinese) [方伟, 孙俊, 谢振平, 须文波 2010 59 3686]

施引文献

[1]	Jung J, Broadwater R P 2014 Renewable and Sustainable Energy Reviews 31 762
[2]	Zhang X Q, Liang J 2012 Acta Phys. Sin. 61 190507 (in Chinese) [张学清, 梁军 2012 61 190507]
[3]	Zhang Y 2013 Chin. Phys. B 22 050502
[4]	Qu H, Ma W T, Zhao J H, Chen B D 2013 Chin. Phys. Lett. 30 110505
[5]	Zhang X Q, Liang J 2013 Acta Phys. Sin. 62 050505 (in Chinese) [张学清, 梁军 2013 62 050505]
[6]	Liu D W, Guo J B, Huang Y H, Wang W S 2013 Proceeding of the CSEE 33 9 (in Chinese) [刘德伟, 郭剑波, 黄越辉, 王伟胜 2013 中国电机工程学报 33 9]
[7]	Wu X S, Zhang B H, Yuan X M, Li G W, Luo G, Zhou Y 2013 Proceeding of the CSEE 33 45 (in Chinese) [吴小珊, 张步涵, 袁小明, 李高望, 罗钢, 周杨 2013 中国电机工程学报 33 45]
[8]	Zhang Z S, Sun Y Z, Gao D W, Lin J, Cheng L 2013 IEEE Trans. on Power Systems 28 3114
[9]	Jiang R W, Wang J H, Zhang M H, Guan Y P 2013 IEEE Trans. on Power Systems 28 2271
[10]	Lange M 2005 J. Sol. Energy Eng. 127 177
[11]	Zhou S L, Mao M Q, Shu J H 2011 Proceeding of the CSEE 31 10 (in Chinese)[周松林, 茆美琴, 苏建徽 2011 中国电机工程学报 31 10]
[12]	Li Z, Han X S, Yang M, Zhong S M 2011 Automation of Electric Power Systems 35 83 (in Chinese) [李智, 韩学山, 杨明, 钟世民 2011 电力系统自动化 35 83]
[13]	Sideratos G, Hatziargyriou N D 2012 IEEE Trans. Power Syst. 27 1788
[14]	Jeon J, Taylor J W 2012 J. Amer. Statist. Assoc. 107 66
[15]	Bessa R J, Miranda V, Botterud A, Zhou Z, Wang J 2012 Renew. Energy 40 29
[16]	Khosravi A, Nahavandi S, Creighton D, Atiya A 2011 IEEE Trans. Neural Networks 22 337
[17]	Huang G B, Zhu Q Y, Siew C K 2006 Neurocomputing 70 489
[18]	Wang X Y, Han M 2012 Acta Phys. Sin. 61 080507 (in Chinese)[王新迎, 韩敏 2012 61 080507]
[19]	Gao G Y, Jiang G P 2012 Acta Phys. Sin. 61 040506 (in Chinese)[高光勇, 梁国平 2012 61 040506]
[20]	Khosravi A, Nahavandi S 2013 IEEE Trans. Sustainable Energy 4 849
[21]	Quan H, Srinivasan D, Khosravi A 2014 IEEE Trans. Neural Netw. Learn. Syst. 25 303
[22]	Van den Bergh F, Engelbrecht A P 2002 Proceedings of the IEEE International Conf on Systems, Man and Cybernetics, October 6-9, 2002 p94
[23]	Sun J, Feng B, Xu W B 2004 Proceedings of the IEEE Congress on Evolutionary Computation, Dec. 1-3, 2004 p325
[24]	Passino K M 2002 IEEE Control Systems Magazine 22 52
[25]	Shrestha D L, Solomatine D P 2006 Neural Netw. 19 225
[26]	Fang W, Sun J, Xie Z P, Xu W B 2010 Acta Phys. Sin. 59 3686 (in Chinese) [方伟, 孙俊, 谢振平, 须文波 2010 59 3686]

[1]	梅英, 谭冠政, 刘振焘, 武鹤. 基于大脑情感学习模型和自适应遗传算法的混沌时间序列预测. , 2018, 67(8): 080502. doi: 10.7498/aps.67.20172104
[2]	沈力华, 陈吉红, 曾志刚, 金健. 基于鲁棒极端学习机的混沌时间序列建模预测. , 2018, 67(3): 030501. doi: 10.7498/aps.67.20171887
[3]	李瑞国, 张宏立, 范文慧, 王雅. 基于改进教学优化算法的Hermite正交基神经网络混沌时间序列预测. , 2015, 64(20): 200506. doi: 10.7498/aps.64.200506
[4]	王新迎, 韩敏. 多元混沌时间序列的多核极端学习机建模预测. , 2015, 64(7): 070504. doi: 10.7498/aps.64.070504
[5]	田中大, 高宪文, 石彤. 用于混沌时间序列预测的组合核函数最小二乘支持向量机. , 2014, 63(16): 160508. doi: 10.7498/aps.63.160508
[6]	陈涵瀛, 高璞珍, 谭思超, 付学宽. 自然循环流动不稳定性的多目标优化极限学习机预测方法. , 2014, 63(20): 200505. doi: 10.7498/aps.63.200505
[7]	赵永平, 张丽艳, 李德才, 王立峰, 蒋洪章. 过滤窗最小二乘支持向量机的混沌时间序列预测. , 2013, 62(12): 120511. doi: 10.7498/aps.62.120511
[8]	赵永平, 王康康. 具有增加删除机制的正则化极端学习机的混沌时间序列预测. , 2013, 62(24): 240509. doi: 10.7498/aps.62.240509
[9]	张文专, 龙文, 焦建军. 基于差分进化算法的混沌时间序列预测模型参数组合优化. , 2012, 61(22): 220506. doi: 10.7498/aps.61.220506
[10]	高光勇, 蒋国平. 采用优化极限学习机的多变量混沌时间序列预测. , 2012, 61(4): 040506. doi: 10.7498/aps.61.040506
[11]	张弦, 王宏力. 具有选择与遗忘机制的极端学习机在时间序列预测中的应用. , 2011, 60(8): 080504. doi: 10.7498/aps.60.080504
[12]	张春涛, 马千里, 彭宏. 基于信息熵优化相空间重构参数的混沌时间序列预测. , 2010, 59(11): 7623-7629. doi: 10.7498/aps.59.7623
[13]	刘福才, 张彦柳, 陈超. 基于鲁棒模糊聚类的混沌时间序列预测. , 2008, 57(5): 2784-2790. doi: 10.7498/aps.57.2784
[14]	张军峰, 胡寿松. 基于多重核学习支持向量回归的混沌时间序列预测. , 2008, 57(5): 2708-2713. doi: 10.7498/aps.57.2708
[15]	贺涛, 周正欧. 基于分形自仿射的混沌时间序列预测. , 2007, 56(2): 693-700. doi: 10.7498/aps.56.693
[16]	蔡俊伟, 胡寿松, 陶洪峰. 基于选择性支持向量机集成的混沌时间序列预测. , 2007, 56(12): 6820-6827. doi: 10.7498/aps.56.6820
[17]	刘福才, 孙立萍, 梁晓明. 基于递阶模糊聚类的混沌时间序列预测. , 2006, 55(7): 3302-3306. doi: 10.7498/aps.55.3302
[18]	叶美盈, 汪晓东, 张浩然. 基于在线最小二乘支持向量机回归的混沌时间序列预测. , 2005, 54(6): 2568-2573. doi: 10.7498/aps.54.2568
[19]	崔万照, 朱长纯, 保文星, 刘君华. 基于模糊模型支持向量机的混沌时间序列预测. , 2005, 54(7): 3009-3018. doi: 10.7498/aps.54.3009
[20]	崔万照, 朱长纯, 保文星, 刘君华. 混沌时间序列的支持向量机预测. , 2004, 53(10): 3303-3310. doi: 10.7498/aps.53.3303

计量

文章访问数: 5977
PDF下载量: 687
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

搜索

留言板