Probabilistic Load Flow Computation Using Non-positive Definite Correlation Control and Latin Hypercube Sampling

XU Qingshan; YANG Yang; HUANG Yu; LIU Jiankun; WEI Peng

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Probabilistic Load Flow Computation Using Non-positive Definite Correlation Control and Latin Hypercube Sampling

更新时间：2025-12-08

- Probabilistic Load Flow Computation Using Non-positive Definite Correlation Control and Latin Hypercube Sampling
- Vol. 44, Issue 7, Pages: 2292-2299(2018)
- 作者机构：
  
  1. 东南大学电气工程学院
  2. 国网江苏省电力公司电力科学研究院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：30 July 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
XU Qingshan, YANG Yang, HUANG Yu, et al. Probabilistic Load Flow Computation Using Non-positive Definite Correlation Control and Latin Hypercube Sampling[J]. 2018, 44(7): 2292-2299.
DOI：

XU Qingshan, YANG Yang, HUANG Yu, et al. Probabilistic Load Flow Computation Using Non-positive Definite Correlation Control and Latin Hypercube Sampling[J]. 2018, 44(7): 2292-2299. DOI：

摘要

大规模分布式电源的并网给潮流计算中拉丁超立方抽样法的应用带来了新的问题。为解决分布式电源的累积分布函数较难获得以及相关性控制中相关系数矩阵非正定两个问题

提出了一种基于修正相关系数矩阵的改进拉丁超立方抽样法。该方法可根据离散数据

进行分层抽样得到样本

并采用正定谱分解法修正使得相关系数矩阵都能进行Cholesky分解

修正速度快误差小

毫秒级的速度便可使误差达到10-4。采用IEEE 33和PG&E69两个节点系统

验证了修正算法的准确性和有效性。仿真结果表明该方法计算速度快

在采样与输出变量的准确性和收敛性方面都要优于蒙特卡罗。

Abstract

The application of the Latin hypercube sampling method in the load flow calculation causes some new problems with the large-scale distributed generators connected to the distribution network. One is that the cumulative distribution function of distributed generators cannot be acquired easily; the other is that the correlation coefficient matrix is not positive-definite in correlation control. To solve these two problems

we proposed a new algorithm. The algorithm is based on the improved Latin hypercube sampling with modified correlation matrix. The algorithm stratifies sampling according to the discrete data and modifies the correlation coefficient matrix by positive definite spectral decomposition after which Cholesky decomposition can be applied to all recovered matrix. The speed of the algorithm is fast and the error can be less than 10-4. The accuracy and effectiveness of the proposed algorithm were proven by the comparative tests in the IEEE 33-bus system and PG&E 69-bus system. The simulation results show that this method is faster

and the accuracy and convergence of the sampled and output variables are better than those of Monte Carlo.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Probabilistic Load Flow Method Based on Spline Reconstruction and Quasi Monte Carlo Simulation

Correlation Analysis on Streaming Electrification and Corrosive Sulfur Concentration in Mineral Insulation Oil Used in Power Transformer

Hybrid Energy Storage Control and System Hierarchical Coordinated Control Strategy for DC Microgrids

Coordination Control of PV Charging Station DC Microgrid System Based on Hybrid Energy Storage Technology

Passivity-based Control of PV Z-source Grid-connected Inverter Based on PCHD Model

Related Author

FANG Sidun

CHENG Haozhong

SONG Yue

ZENG Pingliang

YAO Liangzhong

CAO Xiaolong

YU Qinxue

ZHONG Lisheng

Related Institution

China Electric Power Research Institute

Department of Electrical and Electronic Engineering,the University of Hong Kong

Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University

State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University

State Grid Shaanxi Electric Power Research Institute

AI问答

⁰