Probabilistic Load Flow Analysis Using Randomized Quasi–Monte Carlo Sampling and Johnson Transformation-Reference-Cited by-同舟云学术

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Probabilistic Load Flow Analysis Using Randomized Quasi–Monte Carlo Sampling and Johnson Transformation

Published:2017-12 Issue:6 Volume:143 Page:04017066
ISSN:0733-9402
Container-title:Journal of Energy Engineering
language:en
Short-container-title:J. Energy Eng.

Author:

Gao Shan¹,Liu Yu²^ORCID,Cui Hantao³,Wang Shenzhe⁴

Affiliation:

1. Associate Professor, School of Electrical Engineering, Southeast Univ., Nanjing 210018, China.

2. Ph.D. Candidate, School of Electrical Engineering, Southeast Univ., Nanjing 210018, China (corresponding author). ORCID: .

3. Ph.D. Candidate, Dept. of Electrical Engineering and Computer Science, Univ. of Tennessee, Knoxville, TN 37996.

4. Senior Engineer, State Grid Nanjing Power Supply Company, 1 Aoti Ave., Jianye District, Nanjing 210019, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Waste Management and Disposal,Energy Engineering and Power Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29EY.1943-7897.0000497

Reference34 articles.

1. Probabilistic Load Flow

2. Probabilistic load flow computation with polynomial normal transformation and Latin hypercube sampling

3. Probabilistic load flow computation using Copula and Latin hypercube sampling

5. Probabilistic Load Flow Method Based on Nataf Transformation and Latin Hypercube Sampling

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