电网技术2017，Vol.41Issue(3)：791-798,8.DOI:10.13335/j.1000-3673.pst.2016.1164
基于混合藤Copula模型的风光联合发电相关性建模及其在无功优化中的应用
Modeling of Multi-Dimensional Wind and PV Farm Output Correlation Based on Mixture Vine Copula Structures and its Application in Reactive Power Optimization
摘要
Abstract
To solve problem of describing relevance among outputs of multiple wind and PV farms, this article proposed a mixture model combining K-means clustering method and vine copula structure, considering PV circadian periodicity and using mixture vine copula structure to analyze correlation of daytime data. Backtracking search algorithm is utilized based on this model to accomplish reactive power optimization. The proposed method was verified with simulation in IEEE 30 system with data measured from two wind farms and a PV farm in the United States. Simulation results indicate that the proposed method could turn out accurate description of correlation among multiple wind and PV farms, and get more reliable results of probabilistic load flow.关键词
K-means聚类/混合藤Copula模型/回溯搜索算法/无功优化Key words
K-means cluster/mixture vine Copula structure/backtracking search algorithm/reactive power optimization分类
信息技术与安全科学引用本文复制引用
邱宜彬,欧阳誉波,徐蓓,李奇,陈维荣..基于混合藤Copula模型的风光联合发电相关性建模及其在无功优化中的应用[J].电网技术,2017,41(3):791-798,8.基金项目
国家自然科学基金(61473238,51407146) （61473238,51407146）
四川省杰出青年基金(2015JQ0016). Project Supported by National Natural Science Foundation of China (61473238, 51407146) （2015JQ0016）
Scientific Funds for Outstanding Young Scientists of Sichuan Province (2015JQ0016). （2015JQ0016）
