Power System Uncertainty Modeling Based on Multivariate Gaussian Mixture Model

被引：0

作者：

Gao Y. ^{[1
]}

Xu X. ^{[1
]}

Yan Z. ^{[1
]}

机构：

[1] Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Minhang District, Shanghai

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2023年 / 43卷 / 01期

基金：

中国国家自然科学基金;

关键词：

correlation; Gaussian mixture model; multivariate random variable; power system; uncertainty analysis; wind power;

D O I：

10.13334/j.0258-8013.pcsee.212908

中图分类号：

学科分类号：

摘要：

To overcome the low accuracy problem of the expectation-maximization (EM) algorithm in establishing multivariate probability models based on the Gaussian mixture model (GMM), this paper introduces kernel density estimation (KDE) and the density-preserving hierarchical expectation-maximization (DPHEM) algorithm, and proposes an improved KDE-DPHEM-based algorithm for establishing GMM. The proposed method first uses KDE to build a base GMM and then reduces its component number by DPHEM, which overcomes the problem that the EM algorithm fails to obtain an accurate GMM with large component numbers. Furthermore, to reduce the computational burden of dealing with big data, a hierarchical modeling method according to time scales is proposed; to overcome the combinatorial explosion problem on component numbers for modeling independent random variables, a combination-reduction hierarchical modeling method is also proposed. The proposed methods are tested based on actual wind speed and load data with complicated features. The results show that the proposed methods can obtain a highly accurate GMM, which is superior over the EM-based GMM and Copula functions. © 2023 Chinese Society for Electrical Engineering. All rights reserved.

引用

页码：37 / 47

页数：10

共 26 条

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