全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
电网技术  2015 

基于波动特性的风电出力时间序列建模方法研究

DOI: 10.13335/j.1000-3673.pst.2015.01.032, PP. 208-214

Keywords: 风电波动特性,时间序列,自组织映射聚类,序贯抽样,概率统计

Full-Text   Cite this paper   Add to My Lib

Abstract:

掌握风力发电的随机、波动与间歇特性,并在此基础上构建风电出力时间序列模型对于电力系统规划与运行具有重要意义。提出了一种构造未来风电出力场景的新方法。研究了风电波动过程特性,在极值点处将历史风电出力时间序列划分为波动,采用自组织映射(self-organizationmap,SOM)神经网络将波动聚类为大波动、中波动、小波动和低出力波动。波动变化规律可用高斯函数来定量表达。基于风电波动过程特性阐述了建模方法,将月份按波动出力特性进行分类,分别统计波动类间转移概率和类内统计参数的概率分布,按月序贯抽样风电波动类别与各统计参数,计算并模拟得到风电出力时间序列。对中国某省部分风电场进行了仿真模拟,统计特征参数的对比分析结果验证了上述方法的有效性。

References

[1]  GWEC.全球风电装机数据2013[R].Brussels:GWEC,2013.
[2]  雷亚洲.与风电并网相关的研究课题[J].电力系统自动化,2003,27(8):84-89.Lei Yazhou.Studies on wind farm integration into power system[J].Automation of Electric Power Systems,2003,27(8):84-89(in Chinese).
[3]  Billinton R,Chen H,Ghajar R.A sequential simulation technique for adequacy evaluation of generating systems including wind energy[J].IEEE Trans on Energy Conversion,1996,11(4):728-734.
[4]  Dobakhshari A S,Fotuhi-Firuzabad M.A reliability model of large wind farms for power system adequacy studies[J].IEEE Trans on Energy Conversion,2009,24(3):792-801.
[5]  张宁,周天睿,段长刚,等.大规模风电场接入对电力系统调峰的影响[J].电网技术,2010,34(1):152-158.Zhang Ning,Zhou Tianrui,Duan Changgang,et al.Impact of large-scale wind farm connecting with power grid on peak load regulation demand[J].Power System Technology,2010,34(1):152-158(in Chinese).
[6]  邹斌,李冬.基于有效容量分布的含风电场电力系统随机生产模拟[J].中国电机工程学报,2012,32(7):23-31.Zou Bin,Li Dong.Power system probabilistic production simulation with wind generation based on available capacity distribution[J].Proceedings of the CSEE,2012,32(7):23-31(in Chinese).
[7]  张宁,康重庆,陈治坪,等.基于序列运算的风电可信容量计算方法[J].中国电机工程学报,2011,31(25):1-9.Zhang Ning,Kang Chongqing,Chen Zhiping,et al.Wind power credible capacity evaluation model based on sequence operation[J].Proceedings of the CSEE,2011,31(25):1-9(in Chinese).
[8]  范荣奇,陈金富,段献忠,等.风速相关性对概率潮流计算的影响分析[J].电力系统自动化,2011,35(4):18-22.Fan Rongqi,Chen Jinfu,Duan Xianzhong,et al.Impact of wind speed correlation on probabilistic load flow[J].Automation of Electric Power Systems,2011,35(4):18-22(in Chinese).
[9]  Karaki S H,Salim B A,Chedid R B.Probabilistic model of a two-site wind energy conversion system[J].IEEE Transactions on Energy Conversion,2002,17(4):530-536.
[10]  Chang T P.Estimation of wind energy potential using different probability density functions[J].Applied Energy,2011,88(5):1848-1856.
[11]  李玉敦.计及相关性的风速模型及其在发电系统可靠性评估中的应用[D].重庆:重庆大学,2012.
[12]  Billinton R,Chen Hua,Ghajar R.A sequential simulation technique for adequacy evaluation of generating systems including wind energy.IEEE Transactions on Energy Conversion,1996,11(4):728-734.
[13]  Kitagawaa T,Nomurab T.A wavelet-based method to generate artificial wind fluctuation data[J].Journal of Wind Engineering and Industrial Aerodynamics,2003,91(7):943-964.
[14]  王松岩,于继来,李海峰,等.考虑统计和互相关特性的多风电场风速数据模拟生成方法[J].电力系统自动化,2013,37(6):18-23.Wang Songyan,Yu Jilai,Li Haifeng,et al.A wind speed modeling method for multiple wind farms considering correlation and statistical characteristics[J].Automation of Electric Power Systems,2013,37(6):18-23(in Chinese).
[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(in Chinese).
[16]  刘纯,吕振华,黄越辉,等.长时间尺度风电出力时间序列建模新方法研究[J].电力系统保护与控制,2013,41(1):7-13.Liu Chun,Lü Zhenhua,Huang Yuehui,et al.A new method to simulate wind power time series of large time scale[J].Power System Protection and Control,2013,41(1):7-13(in Chinese).
[17]  Papaefthymiou G,Klockl B.MCMC for wind power simulation[J] IEEE Transactions on Energy Conversion,2008,23(1):234-240.
[18]  Wu T,Ai X,Lin W,et al.Markov chain Monte Carlo method for the modeling of wind power time series[C]//Innovative Smart Grid Technologies-Asia (ISGT Asia).Tianjin,China:IEEE,2012.
[19]  罗钢,石东源,陈金富,等,风光发电功率时间序列模拟的MCMC 方法[J],电网技术,2014,38(2):321-327.Luo Gang,Shi Dongyuan,Chen Jinfu,et al.A Markov chain Monte Carlo method for simulation of wind and solar power time series[J].Power System Technology,2014,38(2):321-327(in Chinese).
[20]  Stephane G M.A theory for multiresolution signal decomposition:The wavelet representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[21]  代倩,段善旭,蔡涛,等.基于天气类型聚类识别的光伏系统短期无辐照度发电预测模型研究[J].中国电机工程学报,2011,31(34):28-35.Dai Qian,Duan Shanxu,Cai Tao,et al.Short-term PV generation system forecasting model without irradiation based on weather type clustering[J].Proceedings of the CSEE,2011,31(34):28-35(in Chinese).
[22]  李智勇,吴晶莹,吴为麟,等.基于自组织映射神经网络的电力用户负荷曲线聚类[J].电力系统自动化,2008,32(15):66-70.Li Zhiyong,Wu Jingying,Wu Weilin,et al.Power customers load profile clustering using the SOM neural network[J].Automation of Electric Power Systems,2008,32(15):66-71(in Chinese).
[23]  刘次华.随机过程[M].武汉:华中科技大学出版社,2001:1-30.
[24]  肖刚,李天柁.系统可靠性分析中的蒙特卡罗方法[M].北京:科学出版社,2003:1-30.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133