基于Nataf变换含相关性的扩展准蒙特卡洛随机潮流方法

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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.

Key words： Probabilistic load flow Nataf transformation extended quasi Monte Carlo method singular value decomposition coefficient matrix

Received: 24 January 2015 Published: 08 February 2017

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TM711

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	Articles by authors
	Fang Sidun
	Cheng Haozhong
	Xu Guodong
	Yao Liangzhong
	Zeng Pingliang

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Fang Sidun,Cheng Haozhong,Xu Guodong等. A Nataf Transformation Based on Extended Quasi Monte Carlo Simulation Method for Solving Probabilistic Load Flow Problems with Correlated Random Variables[J]. Transactions of China Electrotechnical Society, 2017, 32(2): 255-263.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I2/255

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