Research on the correlation of wind farms’ outputs based on fluctuation division and time shift technique

2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2019-12-01 DOI:10.1109/APPEEC45492.2019.8994426

D. Jiang, Yanfeng Ge, N. Chen, Fubao Wu, Chenqi Wang, Yingjun Wu, P. Ju, Feng Wu

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引用次数: 1

Abstract

In order to accurately understand the chrematistics of wind farm’ output, it is necessary to consider the correlation of the outputs of multiple wind farms. In this paper, a method for analyzing the correlation of multiple wind farms’ outputs based on fluctuation division and time shift technology is proposed. Firstly, the overall correlation characteristics of multiple wind farms are obtained by using sequential scatter diagram analysis and regression analysis. Then, the fluctuation process of wind farms is divided and paired by output fluctuation division and output fluctuation pairing algorithm. Finally, the time shift technique of Pearson correlation coefficient and granger causality test are used to obtain the optimal time shift quantity and direction. It can be verified by concrete examples that this method can accurately extract the local characteristics of wind farm output fluctuation, and then more accurately describe the relevant characteristics of wind farm output.

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基于波动分割和时移技术的风电场输出相关性研究

为了准确理解风电场输出的化学性质，需要考虑多个风电场输出的相关性。本文提出了一种基于波动分割和时移技术的多风电场出力相关性分析方法。首先，通过序贯散点图分析和回归分析，得到多个风电场的整体相关特征;然后，采用输出波动分割和输出波动配对算法对风电场波动过程进行分割和配对。最后利用Pearson相关系数时移技术和格兰杰因果检验得到最优时移量和方向。通过具体算例验证，该方法能够准确提取风电场输出波动的局部特征，进而更准确地描述风电场输出的相关特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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