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 International Conference On Renewable Energy Research And Applications
Alcance Proceedings Paper
Naturaleza Científica
Ámbito Internacional
Fecha de publicacion 01/01/2018
ISI 000457681100209
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.
Palabras clave Transient stability assessment; machine learning; extreme learning machine; ensemble models
Miembros de la Universidad Loyola

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