Título A Novel Ensemble Approach for Solving the Transient Stability Classification Problem
Autores BALTAS, NICHOLAS-GREGORY, PERALES GONZÁLEZ, CARLOS, MAZIDI, PEYMAN, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, RODRÍGUEZ CORTÉS, PEDRO, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, RODRÍGUEZ CORTÉS, PEDRO, PERALES GONZÁLEZ, CARLOS, MAZIDI, PEYMAN, BALTAS, NICHOLAS-GREGORY
Publicación externa No
Medio 2018 7th International Conference On Renewable Energy Research And Applications (icrera)
Alcance Conference Paper
Naturaleza Científica
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060621596&doi=10.1109%2fICRERA.2018.8566815&partnerID=40&md5=a4b8ec2f1d4e02a71ccc5c140265e88a
Fecha de publicacion 01/01/2018
Scopus Id 2-s2.0-85060621596
DOI 10.1109/ICRERA.2018.8566815
Abstract As power systems become more complex in order to accommodate distributed generation and increased demand, determining the stability status of a system after a severe contingency is becoming more difficult. To that end, artificial intelligence and machine learning techniques have been studied as a stability prediction tool. Topology changes and data availability however, impose certain limitations towards the generalization of those algorithms, impairing their ability to function in different system conditions. In this paper, we propose a novel ensemble machine-learning model that can maintain high performance in uneven sample class distribution, thus demonstrating resiliency and robustness against false dismissals. © 2018 IEEE.
Palabras clave Learning systems; Machine learning; System stability; Class distributions; Ensemble approaches; Ensemble models; Extreme learning machine; Machine learning models; Machine learning techniques; Stabili
Miembros de la Universidad Loyola

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