Probabilistic load flow computation using Copula and Latin hypercube sampling

被引：114

作者：

Cai, Defu ^{[1
]}

Shi, Dongyuan ^{[1
]}

Chen, Jinfu ^{[1
,2
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China

[2] Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99164 USA

来源：

IET GENERATION TRANSMISSION & DISTRIBUTION | 2014年 / 8卷 / 09期

基金：

中国国家自然科学基金;

关键词：

POWER-FLOW; DEPENDENCE; VARIABLES; SYSTEMS;

D O I：

10.1049/iet-gtd.2013.0649

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];

摘要：

A probabilistic load flow (PLF) method using Copula and improved Latin hypercube sampling is proposed. The stochastic dependence between input random variables is considered. Copula theory is adopted to establish the probability distribution of correlated input random variables. Based on discrete data, an improved Latin hypercube sampling is proposed. The accuracy of probability distribution of correlated input random variables established by Copula theory is evaluated by adopting the power output of wind farms located at New Jersey. The performance of the proposed PLF method is investigated using IEEE 14-bus and IEEE 118-bus test systems.

引用

页码：1539 / 1549

页数：11

共 33 条

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Probabilistic Load Flow in Correlated Uncertain Environment Using Unscented Transformation [J].