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基于频繁模式挖掘的风电爬坡事件统计特性建模及预测
作者:
作者单位:

1.武汉大学电气与自动化学院,湖北省武汉市 430072;2.国家电网公司华北分部,北京市 100053

摘要:

风电爬坡事件的统计特性建模和精准预测有利于电网的安全稳定运行。文中首先通过参数分辨率自适应算法对大型历史风电数据库进行爬坡事件检测,得到风电爬坡事件的历史学习集。对该学习集进行数据挖掘,建立了单个爬坡事件的起点、终点、持续时间以及爬坡间隔的多属性联合统计特性模型,并得到爬坡事件的基本模式。通过关联规则算法建立了多个相邻爬坡事件之间的自相关性统计特性模型。在此基础上,提出了爬坡事件序列预测算法的基本概念和模型。算例结果表明,所提算法能够更为直观地描述爬坡事件的统计特性,且基于事件序列的预测算法能够较好地进行日前的爬坡预测。

关键词:

基金项目:

国家电网公司科技项目(基于数据驱动的大规模风电波动特性建模与功率预测方法研究,520101180052)。

通信作者:

作者简介:

屈尹鹏(1991—),男,博士研究生,主要研究方向:电力系统大数据、风电爬坡事件、配电网优化。E-mail:quyinpeng@whu.edu.cn
徐箭(1980—),男,通信作者,博士,教授,博士生导师,主要研究方向:含大规模风电的电力系统运行调控理论、含高渗透率风电的孤立系统频率电压协调控制技术、基于广域信息的电力系统动态监测与控制关键技术。E-mail:xujian@whu.edu.cn
姜尚光(1990—),男,硕士,工程师,主要研究方向:电力系统调度运行。E-mail:jsgstudent@126.com


Frequent Pattern Mining Based Modeling and Forecasting for Statistical Characteristics of Wind Power Ramp Events
Author:
Affiliation:

1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China;2.North China Branch of State Grid Corporation of China, Beijing 100053, China

Abstract:

The statistical characteristic modeling and accurate forecasting of wind power ramp events are conducive to the safe and stable operation of the power grid. In this paper, first of all, the parameter and resolution adaptive algorithm is used to detect the ramp events in a large-scale historical wind power database, and the history learning set of wind power ramp events is obtained. Data mining is carried out on this learning set to establish a model with multi-attribute joint statistical characteristics of the starting point, ending point, duration, interval ramp for a ramp event, and the basic mode of ramp events is got.The modeling of autocorrelation statistical characteristics between multiple adjacent ramp events is established by using the association rule algorithm. On this basis, the basic concept and model of the forecasting algorithm of ramp event sequence are proposed. The case results show that the statistical characteristics of ramp events can be described more intuitively by the proposed algorithm, and the events sequence based forecasting algorithm can provide better performance for the day-ahead ramp forecasting.

Keywords:

Foundation:
This work is supported by State Grid Corporation of China (No. 520101180052).
引用本文
[1]屈尹鹏,徐箭,姜尚光,等.基于频繁模式挖掘的风电爬坡事件统计特性建模及预测[J].电力系统自动化,2021,45(1):36-43. DOI:10.7500/AEPS20191231007.
QU Yinpeng, XU Jian, JIANG Shangguang, et al. Frequent Pattern Mining Based Modeling and Forecasting for Statistical Characteristics of Wind Power Ramp Events[J]. Automation of Electric Power Systems, 2021, 45(1):36-43. DOI:10.7500/AEPS20191231007.
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  • 收稿日期:2019-12-31
  • 最后修改日期:2020-06-01
  • 录用日期:
  • 在线发布日期: 2021-01-05
  • 出版日期:
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