Articles producció científica> Enginyeria Informàtica i Matemàtiques

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

  • Identification data

    Identifier: imarina:9249219
    Authors:
    Karar MahmoudMohamed Abdel-NasserHeba KashefDomenec PuigMatti Lehtonen
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Karar Mahmoud; Mohamed Abdel-Nasser; Heba Kashef; Domenec Puig; Matti Lehtonen
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed
    Keywords: Power Machine learning photovoltaics neural networks large-scale unbalanced distribution system energy loss
    Abstract: 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: mohamed.abdelnasser@urv.cat
    Author identifier: 0000-0002-1074-2441
    Record's date: 2024-09-07
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.ijimai.org/journal/bibcite/reference/2803
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: International Journal Of Interactive Multimedia And Artificial Intelligence. 6 (4): 157-163
    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
    Article's DOI: 10.9781/ijimai.2020.08.002
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications
  • Keywords:

    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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