期刊文献+

含风电功率时域特性的风电功率序列建模方法 被引量:20

A Wind Power Time Series Modeling Method Based on Its Time Domain Characteristics
下载PDF
导出
摘要 为了能够生成与已有风电功率序列数据特性一致的风特性的改进马尔可夫链蒙特卡罗(Markov chain Monte Carlo,PV-MC)法,即持续与波动蒙特卡罗(persistence and variation-Monte Carlo,PV-MC)法。该方法基于风电功率状态,首先生成满足状态跳变率矩阵的状态序列;而后,利用风电功率状态的持续特性,确定状态序列中状态的持续时间,得到满足持续特性的状态序列;最后,基于波动特性,将状态序列转换为风电功率序列。利用PV-MC方法与传统的MCMC法分别对全球6个不同地区共26座风电场生成风电功率序列,并与原始风电功率序列进行特性对比分析,结果表明:无论在基本统计特性(均值、标准差、概率密度函数和自相关系数)还是在时域特性(持续性和波动性)上,PV-MC法生成的风电功率序列都优于传统的MCMC法所生成的序列。 In order to generate a new wind power time series capturing the time domain features as same as the original wind power time series, an improved Markov Chain Monte Carlo (MCMC) method, referred to as persistence and variation-Monte Carlo(PV-MC) method, was proposed in this paper. The method considered the persistence and variation characteristics of wind power. Firstly, the wind power state series was generated to meet the state transition matrix based on the definition of wind power state. Then the duration time of every state in the series was determined by its duration character. Finally, the variation characteristic was used to convert the state series to wind power series. PV-MC method and the traditional MCMC method had been used to generate wind power time series based on the original wind power time series obtained from 26 wind farms located in 6 various places all over the world. The characteristics of the new time series have been compared with that of the original wind power time series respectively. The results show that PV-MC method is superior to the traditional MCMC method in the conventional statistic characteristics (mean value, variance, probability density function, autocorrelation function) and time domain features (persistence characteristics as well as the variation characteristics).
出处 《中国电机工程学报》 EI CSCD 北大核心 2014年第22期3715-3723,共9页 Proceedings of the CSEE
基金 国家863高技术基金项目(2011AA05A112) 国家自然科学基金重点项目(50937002) 中国博士后特别资助项目(2013T60717)~~
关键词 风电功率序列 马尔科夫链 蒙特卡罗法 持续特性 波动特性 wind power time series Markov chain MonteCarlo method persistence characteristics variationcharacteristics
  • 相关文献

参考文献21

  • 1Chai Chompoo-inwai,Lee W J,Fuangfoo P,et al.System impact study for the interconnection of wind generation and utility system[J].IEEE Transactions on Industry Applications,2005,41(1):163-168. 被引量:1
  • 2张丽英,叶廷路,辛耀中,韩丰,范高锋.大规模风电接入电网的相关问题及措施[J].中国电机工程学报,2010,30(25):1-9. 被引量:648
  • 3朱星阳,刘文霞,张建华.考虑大规模风电并网的电力系统随机潮流[J].中国电机工程学报,2013,33(7):77-85. 被引量:94
  • 4Nichita C,Luca D,Dakyo B,et al.Large band simulation of the wind speed for real time wind turbine simulators [J].IEEE Transactions on Energy Conversion,2002,17(4):523-529. 被引量:1
  • 5李东东,陈陈.风力发电系统动态仿真的风速模型[J].中国电机工程学报,2005,25(21):41-44. 被引量:109
  • 6Lojowska A,Kurowicka D,Papaefthymiou G,et al.Advantages of ARMA-GARCH wind speed time series modeling[C]//IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).Singapore:IEEE,2010:83-88. 被引量:1
  • 7Kitagawa T,Nomura T.A wavelet-based method to generate artificial wind ?uctuation data[J].Journal of wind engineering and industrial aerodynamics,2003,91(7):943-964. 被引量:1
  • 8Papaefthymiou G,Klockl B.MCMC for wind power simulation[J].IEEE Transactions on Energy Conversion,2007,23(1):234-240. 被引量:1
  • 9Chen P,Pedersen T,Bak-Jensen B,et al.ARIMA-Based time series model of stochastic wind power generation [J].IEEE Transactions on Power System,2010,25(2):667-676. 被引量:1
  • 10Chen P,Berthelsen K K,Bak-Jensen B,et al.Markov model of wind power time series using Bayesian inference of transition matrix[C]//35th Annual Conference of IEEE on Industrial Electronics.Porto:IEEE,2009:627-632. 被引量:1

二级参考文献98

共引文献1547

同被引文献247

引证文献20

二级引证文献359

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部