A Novel Probabilistic Optimal Power Flow Model With Uncertain Wind Power Generation Described by Customized Gaussian Mixture Model

By D. Ke, C. Chung, Yuanzhan Sun. IEEE Transactions on Sustainable Energy, 2016

105 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c65dc4fd5d8cf2b5e73bb938e6a921edf8358563

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

Citations in the corpus

• Statistical Representation of Wind Power Ramps Using a Generalized Gaussian Mixture Model (2018, methodology): “[13] customized 66 the GMM by three Gaussian functions and utilized the GMM 67 to approximate the probability density function (PDF) of wind 68 power generation with triple probability peaks.”
• Chance-Constrained Economic Dispatch With Non-Gaussian Correlated Wind Power Uncertainty (2017, methodology): “Reference [2] adopts the “customized GMM” to represent the distribution of power output of a single wind farm.”