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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).

Get Citation
[1]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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History
  • Received:December 31,2019
  • Revised:June 01,2020
  • Adopted:
  • Online: January 05,2021
  • Published: