NDLI: Probabilistic load flow evaluation with hybrid Latin Hypercube Sampling and multiple linear regression

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyuan Xu Zheng Yan
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Shanghai Jiao Tong Univ., Shanghai, China (Xiaoyuan Xu; Zheng Yan)
Abstract	Probabilistic load flow (PLF) has gained wide attention in power system planning and operation as an efficient tool to analyze the influences of random variables. In this paper, Latin Hypercube Sampling combined with multiple linear regression (LHS-MLR) is proposed to solve PLF evaluation considering correlated variables. Based on the relationship between linear correlation and linear regression, an algorithm is designed to arrange the orders of samples to introduce desired dependences among variables. The proposed method is compared with NORTA and Genetic Algorithm using IEEE 9-bus and IEEE 118-bus systems. Simulation results indicate that LHS-MLR is a promising method in PLF evaluation.
Starting Page	1
Ending Page	5
File Size	1283493
Page Count	5
File Format	PDF
ISBN	9781467380409
DOI	10.1109/PESGM.2015.7285724
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wind speed Benchmark testing Annealing Probability distribution Probabilistic logic Hypercubes Mathematical model probabilistic load flow Latin Hypercube Sampling linear correlation multiple linear regression
Content Type	Text
Resource Type	Article

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