| 并网型大型风电场风力发电功率-时间序列的混沌属性分析 |
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| 引用本文： | 王丽婕,廖晓钟,高爽,冬雷. 并网型大型风电场风力发电功率-时间序列的混沌属性分析[J]. 北京理工大学学报, 2007, 27(12): 1077-1080 |
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| 作者姓名： | 王丽婕 廖晓钟 高爽 冬雷 |
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| 作者单位： | 北京理工大学,信息科学技术学院自动控制系,北京,100081;北京理工大学,信息科学技术学院自动控制系,北京,100081;复杂系统智能控制与决策教育部重点实验室,北京,100081 |
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| 摘 要： | 为了合理调度大型并网型风力发电系统中的供电系统,降低供电系统的旋转备用容量和运行成本,对风力发电容量进行预测.利用非线性动力学的理论方法对并网型风力发电系统的发电容量-时间序列进行分析,以检验其是否存在混沌属性.通过对风力发电容量-时间序列进行低维非线性动力学建模,分析该时间序列呈现的混沌特性,该结果为基于混沌时间序列的风力发电容量预测奠定了基础.
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| 关 键 词： | 风力发电 混沌属性 功率预测 |
| 文章编号： | 1001-0645（2007）12-1077-04 |
| 收稿时间： | 2007-05-10 |
| 修稿时间： | 2007-05-10 |
Chaos Characteristics Analysis of Wind Power Generation Time Series for a Grid Connecting Wind Farm |
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| WANG Li-jie,LIAO Xiao-zhong,GAO Shuang and DONG Lei. Chaos Characteristics Analysis of Wind Power Generation Time Series for a Grid Connecting Wind Farm[J]. Journal of Beijing Institute of Technology(Natural Science Edition), 2007, 27(12): 1077-1080 |
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| Authors: | WANG Li-jie LIAO Xiao-zhong GAO Shuang DONG Lei |
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| Affiliation: | Department of Automatic Control,School of Information Science and Technology,Beijing Institute of Technology,Beijing 100081,China;Department of Automatic Control,School of Information Science and Technology,Beijing Institute of Technology,Beijing 100081,China; Key Laboratory of Complex System Intelligent Control and Decision of Ministry of Education,Beijing 100081,China;Department of Automatic Control,School of Information Science and Technology,Beijing Institute of Technology,Beijing 100081,China;Department of Automatic Control,School of Information Science and Technology,Beijing Institute of Technology,Beijing 100081,China; Key Laboratory of Complex System Intelligent Control and Decision of Ministry of Education,Beijing 100081,China |
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| Abstract: | In a grid connection wind power generation system, it is very important to predict the wind power generation capacity in scheduling the system and achieving low spinning reserve and optimal operating cost. The time series of wind power generating capacity are examined by nonlinear dynamical methods, in order to identify the chaos characteristics from its random-like waveform. Analysis of modeling with low dimension nonlinear dynamics indicates that the time series of wind power generation capacity have chaos characteristic, and that wind power-generating capacity can be predicted in a short time. |
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| Keywords: | wind power generation chaos characteristics power prediction |
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