电力系统自动化2016，Vol.40Issue(16)：23-30,8.DOI:10.7500/AEPS20151207007
采用混合高斯模型及边缘变换技术的蒙特卡洛随机潮流方法
Probabilistic Load Flow Method Using Monte Carlo Simulation Based on Gaussian Mixture Model and Marginal Transformation
摘要
Abstract
A probabilistic load flow method considering the correlation between input variables based on improved Monte Carlo simulation ( MCS) is proposed . Regarding the behaviors of diversity and randomness for variable input , the method establishes the Gaussian mixture model ( GMM ) for variable input and performs parameter estimation by the use of measured data . Uniform design sampling ( UDS) is introduced to improve the sampling efficiency , and correlated samples are generated by marginal transformation and Cholesky decomposition . Moreover , multi‐linearization is applied to reduce the truncated error as well as time consumption . The simulation results of IEEE 30‐bus and IEEE 118‐bus test system verify the effectiveness , accuracy and practicability of the proposed method .关键词
随机潮流/混合高斯模型/相关性/均匀设计抽样/多重线性化Key words
probabilistic load flow/Gaussian mixture model/correlation/uniform design sampling/multi-linearization引用本文复制引用
徐青山,黄煜,刘建坤,卫鹏..采用混合高斯模型及边缘变换技术的蒙特卡洛随机潮流方法[J].电力系统自动化,2016,40(16):23-30,8.基金项目
国家自然科学基金资助项目（51377021）；中央高校基本科研业务费专项资金资助项目（2242016K41064）；国家电网公司科技项目“新能源发电预测误差对电网安全运行影响评价方法研究”。 （）
