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Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics

  • Datos identificativos

    Identificador: imarina:9249219
    Autores:
    Karar MahmoudMohamed Abdel-NasserHeba KashefDomenec PuigMatti Lehtonen
    Resumen:
    In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale distribution systems with a fast-computational speed. In this paper, we propose an efficient method for performing time-series simulations for unbalanced power distribution systems with PV. Unlike the existing iterative methods, the proposed method is based on machine learning. Specifically, we propose a fast, reliable and accurate method for determining energy losses in distribution systems with PV. The proposed method is applied to a large-scale unbalanced distribution system (the IEEE 906 Bus European LV Test Feeder) with PV grid-connected units. The method is validated using OpenDSS software. The results demonstrate the high accuracy and computational performance of the proposed method.
  • Otros:

    Autor según el artículo: Karar Mahmoud; Mohamed Abdel-Nasser; Heba Kashef; Domenec Puig; Matti Lehtonen
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed
    Palabras clave: Power Machine learning photovoltaics neural networks large-scale unbalanced distribution system energy loss
    Resumen: In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale distribution systems with a fast-computational speed. In this paper, we propose an efficient method for performing time-series simulations for unbalanced power distribution systems with PV. Unlike the existing iterative methods, the proposed method is based on machine learning. Specifically, we propose a fast, reliable and accurate method for determining energy losses in distribution systems with PV. The proposed method is applied to a large-scale unbalanced distribution system (the IEEE 906 Bus European LV Test Feeder) with PV grid-connected units. The method is validated using OpenDSS software. The results demonstrate the high accuracy and computational performance of the proposed method.
    Áreas temáticas: Statistics and probability Signal processing Linguística e literatura Interdisciplinar Engenharias iv Educação Computer vision and pattern recognition Computer science, interdisciplinary applications Computer science, artificial intelligence Computer science applications Computer networks and communications Ciência da computação Artificial intelligence
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: mohamed.abdelnasser@urv.cat
    Identificador del autor: 0000-0002-1074-2441
    Fecha de alta del registro: 2024-09-07
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.ijimai.org/journal/bibcite/reference/2803
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: International Journal Of Interactive Multimedia And Artificial Intelligence. 6 (4): 157-163
    Referencia de l'ítem segons les normes APA: Karar Mahmoud; Mohamed Abdel-Nasser; Heba Kashef; Domenec Puig; Matti Lehtonen (2020). Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics. International Journal Of Interactive Multimedia And Artificial Intelligence, 6(4), 157-163. DOI: 10.9781/ijimai.2020.08.002
    DOI del artículo: 10.9781/ijimai.2020.08.002
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Computer Science, Artificial Intelligence,Computer Science, Interdisciplinary Applications,Computer Vision and Pattern Recognition,Signal Processing,Statistics and Probability
    Power
    Machine learning
    photovoltaics
    neural networks
    large-scale unbalanced distribution system
    energy loss
    Statistics and probability
    Signal processing
    Linguística e literatura
    Interdisciplinar
    Engenharias iv
    Educação
    Computer vision and pattern recognition
    Computer science, interdisciplinary applications
    Computer science, artificial intelligence
    Computer science applications
    Computer networks and communications
    Ciência da computação
    Artificial intelligence
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