Analytical method based on improved Gaussian mixture model for probabilistic load flow

被引：0

作者：

Li C. ^{[1
]}

Wang T. ^{[1
]}

Xiang Y. ^{[1
]}

Wang Z. ^{[1
]}

Shi B. ^{[2
]}

Zhang Y. ^{[3
]}

机构：

[1] State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing

[2] Beijing Sifang Automation Co., Ltd., Beijing

[3] State Grid Beijing Electric Power Research Institute, Beijing

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2020年 / 48卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Correlation; Gaussian mixture model; Genetic algorithm; Joint distribution; Probabilistic load flow;

D O I：

10.19783/j.cnki.pspc.190778

中图分类号：

学科分类号：

摘要：

The random nature of a power system is accentuated by large-scale wind generation. Probabilistic load flow is an important tool for steady-state operation evaluation analysis that takes into account the random nature of the system. Considering the random nature and correlation of output power of several wind farms, a probability model based on Gaussian mixture model improved by genetic algorithm is proposed, which can exactly characterize the random nature and correlation of renewable generation. On this basis, the joint probability density function and joint cumulative distribution function of transmission lines are derived by a load flow equation, which obtains the results of probabilistic load flow. Simulation results demonstrate that the proposed method gives high accuracy and high speed. The method can assess the risk of multiple lines being overloaded simultaneously. © 2020, Power System Protection and Control Press. All right reserved.

引用

页码：146 / 155

页数：9

共 28 条

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