Probabilistic load flow with non-Gaussian correlated random variables using Gaussian mixture models

By Gustavo Valverde, Andrija T. Saric, Vladimir Terzija. 2012

88 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a89514f0f33d08f1066cc9660e909116b91e0d1b

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. Feng Liu
2. Chen Shen
3. Zhenke Wang
4. Mingjian Cui
5. Jie Zhang
6. Cong Feng
7. Chen-Ching Liu

In Safety, Risk, Reliability and Quality

1. Feng Liu
2. Zhenke Wang
3. Mingjian Cui
4. Jie Zhang
5. Cong Feng
6. Chen-Ching Liu

In Statistics and Probability

1. Feng Liu
2. Chen Shen
3. Zhiwen Wang
4. Xiangyu Wu
5. Feng Gao
6. Chen-Ching Liu

Citations in the corpus

• Statistical Representation of Wind Power Ramps Using a Generalized Gaussian Mixture Model (2018, methodology): “[15] proposed the use of GMM to represent non-Gaussian correlated wind power output and aggregated load demands for modeling the probabilistic load flow.”
• A Novel Probabilistic Optimal Power Flow Model With Uncertain Wind Power Generation Described by Customized Gaussian Mixture Model (2016, unknown): “, five in [30]) to obtain satisfactory approximation effect for the pdf of WPG because it does not care about the discontinuities of the pdf.”
• Chance-Constrained Economic Dispatch With Non-Gaussian Correlated Wind Power Uncertainty (2017, methodology): “, [2], [31], apply the GMM to represent wind power”