Título Support Vector Machine and Neural Network Applications in Transient Stability
Autores BALTAS, NICHOLAS-GREGORY, MAZIDI, PEYMAN, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, RODRÍGUEZ CORTÉS, PEDRO, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, RODRÍGUEZ CORTÉS, PEDRO, 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 000457681100164
Abstract Phasor measurement units and wide area measurement systems are becoming more and more popular due to their capability to record operational data with high sampling rates. By storing and processing this large amount of data, faster and more reliable approaches can be developed that overcome some of the drawbacks of traditional methods, such as response speed and accuracy. Many research studies use pattern recognition methods and machine learning techniques to predict the stability of a system following disturbances (unpredicted events). This paper aims to deliver a review of research work carried out in recent years for the assessment of transient stability by focusing particularly on the machine learning techniques. Specifically, supervised and unsupervised learning techniques such as support vector machines, neural networks including hybrid and ensemble models. Moreover, the methodologies including data generation, feature selection and validation are also reviewed.
Palabras clave transient stability; neural network; support vector machine; hybrid models; ensemble models
Miembros de la Universidad Loyola

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