| Título |
A Comparative Analysis of Decision Trees, Support Vector Machines and Artificial Neural Networks for On-line Transient Stability Assessment |
| Autores |
BALTAS, NICHOLAS-GREGORY, MAZIDI, PEYMAN, Ma, Jin , FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, RODRÍGUEZ CORTÉS, PEDRO, IEEE |
| Publicación externa |
No |
| Alcance |
Conference Paper |
| Naturaleza |
Científica |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056545804&doi=10.1109%2fSEST.2018.8495872&partnerID=40&md5=3f83791c4ce21c16433d6df71936516c |
| Fecha de publicacion |
01/01/2018 |
| ISI |
000450802300094 |
| Scopus Id |
2-s2.0-85056545804 |
| DOI |
10.1109/SEST.2018.8495872 |
| Abstract |
Transient instability is considered the most severe form of instability in power systems with grave socioeconomic repercussions if not prevented. Conventional methods, such as time domain simulations and direct methods impose limitations to fast on-line transient stability assessment in modern power systems. The development of phasor measurement units paved the way for transient stability assessment by means of artificial intelligence for pattern recognition and classification. Many classification algorithms have been reported in the literature for assessing transient stability. This paper aims to provide insights regarding which algorithm is more suitable for a given dataset for power system stability assessment. For this purpose, decision trees, support vector machines and artificial neural networks are investigated for their ability to address the binary stability classification problem in a comparative analysis for two datasets. The two datasets differ in terms of class distribution so that the impact of imbalanced datasets on classification accuracy could also be studied. The above datasets are created using MATLAB with two extension packages of MATPOWER and MATDYN to simulate different contingency scenarios in IEEE-9 bus test system. |
| Palabras clave |
Transient Stability Assessment; Decision Trees; Support Vector Machines; Artificial Neural Networks; Imbalanced Datasets; Machine Learning |
| Miembros de la Universidad Loyola |
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