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超短期风电功率爬坡事件检测和统计分析
引用本文:杨茂,马剑,李大勇,刘红柳,孙涌. 超短期风电功率爬坡事件检测和统计分析[J]. 电力系统保护与控制, 2018, 46(6): 62-68
作者姓名:杨茂  马剑  李大勇  刘红柳  孙涌
作者单位:东北电力大学电气工程学院,吉林 吉林 132012,东北电力大学电气工程学院,吉林 吉林 132012,国网吉林省电力有限公司通化供电公司, 吉林 通化 132022,东北电力大学电气工程学院,吉林 吉林 132012,国网淄博供电公司,山东 淄博 255000
基金项目:国家重点基础研究发展计划项目(973计划)(2013CB228201);国家自然科学基金项目(51307017)
摘    要:随着风电渗透率的逐渐增加,超短期风电功率爬坡事件对电力系统的影响愈来愈显著。当前国内对爬坡事件没有明确定义,且缺少相应的检测方法和统计分析。阐述了爬坡事件的定义,提出了一种超短期风电功率爬坡事件检测方法,并从爬坡持续时间、爬坡变化率和爬坡幅值三个方面对上爬坡和下爬坡两种爬坡类型进行了统计。最后分析了超短期风电功率爬坡事件的日、月分布规律。实例证明,所提出的检测方法可以快速准确地检测出风电功率爬坡事件及其特征值。统计结果表明,上爬坡事件和下爬坡事件的爬坡持续时间、爬坡变化率和爬坡幅值三个爬坡特征具有较高对称性,但两类爬坡事件高发在一天之中不同的时段,也表现出明显的日、月分布特征。

关 键 词:超短期;风电功率;爬坡事件;检测;统计分析
收稿时间:2017-03-03
修稿时间:2017-06-06

Ultra-short-term wind power climbing event detection and statistical analysis
YANG Mao,MA Jian,LI Dayong,LIU Hongliu and SUN Yong. Ultra-short-term wind power climbing event detection and statistical analysis[J]. Power System Protection and Control, 2018, 46(6): 62-68
Authors:YANG Mao  MA Jian  LI Dayong  LIU Hongliu  SUN Yong
Affiliation:School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China,School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China,Tonghua Power Supply Company, State Grid Jilin Electric Power Corporation, Tonghua 132022, China,School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China and State Grid Zibo Power Supply Company, Zibo 255000, China
Abstract:With the increase of wind power penetration, the ultra-short-term wind power climbing events exert more and more significant influence on power system. The domestic definition of climbing event is not yet clear at present, and lack of the corresponding detection methods and statistical analysis. This paper expounds the definition of climbing events, proposes a ultra-short-term wind power climbing event detection method, and makes a statistic in terms of ramp duration, rate, and swing for up-ramps and down-ramps. Finally, it analyzes the ultra-short-term wind power climbing events distribution regularity in the form of daily cycles and yearly cycles. Instances prove that the proposed detection method can rapidly and accurately detect the wind power climbing event and its characteristic value. Statistical calculation shows that the ramp duration, rate, and swing of the up-ramps and down-ramps have high symmetry, but the high incidence of the two types of climbing events happen in different period of the day, they also show an obvious daily and monthly distribution characteristics. This work is supported by National Major Basic Research Program (973 Program) (No. 2013CB228201) and National Natural Science Foundation of China (No. 51307017).
Keywords:ultra-short-term   wind power   climbing event   detection   statistical analysis
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