风电功率状态的时域概率特性研究

韩杏宁; 黎嘉明; 文劲宇; 谢海莲; 岳程燕

引用本文:	韩杏宁,黎嘉明,文劲宇,等.风电功率状态的时域概率特性研究[J].电力系统保护与控制,2016,44(14):31-39.
	HAN Xingning,LI Jiaming,WEN Jinyu,et al.Research on the time domain probabilistic characteristics of wind power state[J].Power System Protection and Control,2016,44(14):31-39

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风电功率状态的时域概率特性研究
韩杏宁¹,黎嘉明¹,文劲宇¹,谢海莲²,岳程燕²
(1.强电磁工程与新技术国家重点实验室(华中科技大学电气与电子工程学院)，湖北武汉 430074; 2..ABB(中国)有限公司研究中心，北京 100016)

摘要:

随着大规模风电的并网，深入认识风电功率的随机特性将有利于更好地预测和利用风电。目前，对于风电功率波动特性的研究较多，对风电功率状态的时域概率特性的研究更侧重于对风电状态转移概率特性的描述。基于风电功率状态的定义，深入研究了风电功率状态持续时间的概率分布描述函数和状态转移概率矩阵。基于多座风电场/群的大量实测功率数据的研究发现:风电功率在某个特定状态可能持续几个小时甚至更长时间，逆高斯分布较适合用于描述风电功率状态持续时间的概率分布，可为系统运行调度风电提供参考信息；风电功率状态转移概率矩阵量化了风电场功率状态的跳变程度，风电功率状态的跳变呈现山脊特性。

关键词: 风电功率状态概率特性持续时间逆高斯分布转移概率矩阵

DOI：10.7667/PSPC151329

分类号:

基金项目:国家“973”重点基础研究发展计划项目 (2012CB215106);ABB公司(中国)资助项目

Research on the time domain probabilistic characteristics of wind power state

HAN Xingning¹,LI Jiaming¹,WEN Jinyu¹,XIE Hailian²,YUE Chengyan²

(1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology (School of Electric and
Electronic Engineering, Huazhong University of Science and Technology), Wuhan 430074, China;
;2. Research Center, ABB (China) Co., Ltd., Beijing 100016, China)

Abstract:

With the large scale integration of wind power, having a deep understanding about the stochastic characteristics can lead to more precise forecasting and better utilization on wind power. Wind power fluctuation has received well attention while the probabilistic characteristics regarding wind power states are still under progress. This paper first presents the definition of wind power states, and then studies the probabilistic descriptions of the duration time within one power state and transition between different power states employing massive measured wind power data. The duration time within one wind power state is revealed to follow inverse Gaussian distribution, which is useful for power system operation. The matrix of the transition probability between wind power states quantifies the degree of transition and shows a ridge pattern. The gradient of the ridge is related to the time scale of interest. This work is supported by National Basic Research Program of China (No. 2012CB215106) and ABB Co., Ltd. Research Project.

Key words: wind power state probabilistic characteristics duration time inverse Gaussian distribution transition probability matrix

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