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 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-s2.0-85060645334&doi=10.1109%2fICRERA.2018.8567024&partnerID=40&md5=16856f8158224fa61d238d8352808c7a
Fecha de publicacion 01/01/2018
Scopus Id 2-s2.0-85060645334
DOI 10.1109/ICRERA.2018.8567024
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 that the researchers followed to develop such models including data generation, feature selection and validation are also reviewed. © 2018 IEEE.
Palabras clave Learning algorithms; Machine learning; Neural networks; Pattern recognition systems; Phasor measurement units; Stability; Support vector machines; Ensemble models; High sampling rates; Hybrid model; M
Miembros de la Universidad Loyola

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