# [1]黄煜,徐青山,刘建坤,等.基于混合高斯模型的相关非高斯输入变量随机潮流计算[J].东南大学学报(自然科学版),2017,47(2):291-298.[doi:10.3969/j.issn.1001-0505.2017.02.016] 　Huang Yu,Xu Qingshan,Liu Jiankun,et al.Probabilistic load flow with non-Gaussian correlated input variables based on Gaussian mixture model[J].Journal of Southeast University (Natural Science Edition),2017,47(2):291-298.[doi:10.3969/j.issn.1001-0505.2017.02.016] 点击复制 基于混合高斯模型的相关非高斯输入变量随机潮流计算() 分享到： var jiathis_config = { data_track_clickback: true };

47

2017年第2期

291-298

2017-03-20

## 文章信息/Info

Title:
Probabilistic load flow with non-Gaussian correlated input variables based on Gaussian mixture model

1东南大学电气工程学院, 南京 210096; 2江苏省电力公司电力科学研究院, 南京 210003
Author(s):
1School of Electrical Engineering, Southeast University, Nanjing 210096, China
2Jiangsu Electric Power Research Institute, Nanjing 210003, China

Keywords:

TM74
DOI:
10.3969/j.issn.1001-0505.2017.02.016

Abstract:
An algorithm for probabilistic load flow considering the correlation between input variables was proposed. A Gaussian mixture model(GMM)was established by the algorithm to represent non-Gaussian input variables in the system. On such a basis, a Gaussian component combination method(GCCM)was introduced and the marginal distribution of any output variable was directly obtained from multiple weighted least square runs(WLS). A study was also carried out to reduce the number of trials by limiting the number of Gaussian components. The simulation and error analysis on IEEE-30 test system indicated that GMM had the features of high fitting precision and wide applicability. The results obtained from the proposed method are identical to that of MCS and the computational efficiency is obviously improved. The effectiveness and the accuracy are proved to be closely related to operation times of WLS.

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