[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]
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基于混合高斯模型的相关非高斯输入变量随机潮流计算()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第2期
页码:
291-298
栏目:
电气工程
出版日期:
2017-03-20

文章信息/Info

Title:
Probabilistic load flow with non-Gaussian correlated input variables based on Gaussian mixture model
作者:
黄煜1徐青山1刘建坤2卫鹏2
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):
Huang Yu1 Xu Qingshan1 Liu Jiankun2 Wei Peng2
1School of Electrical Engineering, Southeast University, Nanjing 210096, China
2Jiangsu Electric Power Research Institute, Nanjing 210003, China
关键词:
混合高斯模型 约简算法 相关性 高斯分量组合 随机潮流
Keywords:
Gaussian mixture model reduction algorithm correlation Gaussian component combination probabilistic load flow
分类号:
TM74
DOI:
10.3969/j.issn.1001-0505.2017.02.016
摘要:
提出一种考虑输入变量相关性的随机潮流计算方法.该方法针对系统中的非高斯输入变量,建立其混合高斯模型(GMM).在此基础上,引入高斯分量组合算法(GCCM),通过多次加权最小二乘计算(WLS)直接求得输出变量的概率分布.研究限制GMM中高斯分量个数的约简方法,以减少WLS运算次数.对IEEE-30节点系统的仿真和误差分析表明,GMM具有拟合精度高、适用性广的特点.所提方法与MCS的计算结果基本一致,但计算效率有了显著提高,并且算法的速度和精度与WLS运算次数有关.
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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备注/Memo

备注/Memo:
收稿日期: 2016-07-19.
作者简介: 黄煜(1992—),男,博士生;徐青山(联系人),男,博士,教授,博士生导师,xuqingshan@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51577028)、国家电网公司科技资助项目.
引用本文: 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.
更新日期/Last Update: 2017-03-20