Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements

By F. Gomez, Athula D. Rajapakse, U. Annakkage, Ioni T. Fernando. IEEE Transactions on Power Systems, 2011

327 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0b70a239404ec29b68e0adba3aacb74d3ef5c895

How researchers see this paper

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

In Control and Systems Engineering

1. Victor O. K. Li
2. James Jian Qiao Yu
3. David John Hill
4. Mladen Kezunovic
5. Yunhe Hou

In Electrical and Electronic Engineering

1. James Jian Qiao Yu
2. Victor O. K. Li
3. Albert Y. S. Lam
4. David John Hill
5. Mladen Kezunovic
6. Jiatao Gu
7. Yunhe Hou

Citations in the corpus

• Intelligent Time-Adaptive Transient Stability Assessment System (2016, methodology): “This index is widely employed as a transient stability index in the previous literature, see [16], [25] for some references.”
• Regression tree for stability margin prediction using synchrophasor measurements (2013, background): “A review of literature reveals that several other data mining tools such as multi-linear regression, neural networks, and support vector machine have been used to evaluate the system stability status [12]–[14].”