Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition

By Han Yu, C. Chung, Kit Po Wong, H. Lee, J. H. Zhang. IEEE Transactions on Power Systems, 2009

430 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/4498c406246ca4a25c7cb3161067b91bf6257664

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Chongqing Kang
2. Feng Liu
3. Chen Shen
4. Jingwei Yang
5. Yi Wang
6. Qixin Chen
7. Alireza Soroudi
8. Turaj Amraee

In Safety, Risk, Reliability and Quality

1. Chongqing Kang
2. Feng Liu
3. Jingwei Yang
4. Yi Wang
5. Qixin Chen
6. Ning Zhang
7. Chen-Ching Liu
8. Jun-hui Huang

Citations in the corpus

• Dependent Discrete Convolution Based Probabilistic Load Flow for the Active Distribution System (2017, methodology): “For the situation in which variables have non-Gaussian marginal distributions, inverse transformation is conducted after Cholesky decomposition and Monte Carlo [15] or Latin hypercube sampling [16] to ensure that the variables follow their respective marginal distributions.”
• Chance-Constrained Economic Dispatch With Non-Gaussian Correlated Wind Power Uncertainty (2017, methodology): “, Monte Carlo Simulation (MCS) [4], point estimation [5], and cumulant-based series expansion [6], these methods need improvement in efficiency and accuracy [7].”
• Decision making under uncertainty in energy systems: state of the art (2013, methodology): “There are some methods for reducing the computational burden of MCS like Latin Hypercube Sampling (LHS) [9].”