A Nataf Transformation Based on Extended Quasi Monte Carlo Simulation Method for Solving Probabilistic Load Flow Problems with Correlated Random Variables
Fang Sidun1, Cheng Haozhong1, Xu Guodong1, Yao Liangzhong2, Zeng Pingliang2
1. School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China; 2. China Electric Power Research Institute Beijing 100192 China
Abstract:For probabilistic load flow (PLF) analysis, quasi Monte Carlo (QMC) simulation method has higher efficiency than Latin Hypercube Sampling, and its extension is more flexible. Therefore, a Nataf transformation based extended QMC (NEQM) method is proposed to solve the PLF problems. During the process, the extended procedure enlarges the sample size with arbitrary step, and then the Nataf transformation is performed to reconstruct the probability distributions of input variables. With the increase of sample size, this method retains the load flow results already obtained. Furthermore, singular value decomposition method is employed to control the correlation matrix before and after the extension in place of Cholesky decomposition, which is able to handle the non-positive definite correlation matrixes, simultaneously. Through IEEE 30 and IEEE 118 bus systems, the proposed method shows its efficiency and accuracy in comparison with Simple Random Sampling and Nataf Transformation based Extended LHS. The simulation results suggest that, NEQM enhance the accuracy of obtained variables especially the standard deviations with shorter consuming time.
方斯顿, 程浩忠, 徐国栋, 姚良忠, 曾平良. 基于Nataf变换含相关性的扩展准蒙特卡洛随机潮流方法[J]. 电工技术学报, 2017, 32(2): 255-263.
Fang Sidun, Cheng Haozhong, Xu Guodong, Yao Liangzhong, Zeng Pingliang. A Nataf Transformation Based on Extended Quasi Monte Carlo Simulation Method for Solving Probabilistic Load Flow Problems with Correlated Random Variables. Transactions of China Electrotechnical Society, 2017, 32(2): 255-263.
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