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

被引：0
|
作者：
Xu Q. [1 ]
Yang Y. [1 ]
Huang Y. [1 ]
Liu J. [2 ]
Wei P. [2 ]
机构：
[1] School of Electrical Engineering, Southeast University, Nanjing
[2] State Grid Jiangsu Electric Power Research Institute, Nanjing
来源：
Yang, Yang (yangyang9296@163.com) | 2018年 / Science Press卷 / 44期
基金：
中国国家自然科学基金;
关键词：
Cholesky decomposition; Correlation; Latin hypercube sampling; Non-positive definite matrix; Photovoltaic; Probabilistic load flow;
D O I：
10.13336/j.1003-6520.hve.20180628024
中图分类号：
学科分类号：
摘要：
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. © 2018, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：2292 / 2299
页数：7
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