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  • 标题:Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics
  • 本地全文:下载
  • 作者:Karar Mahmoud ; Mohamed Abdel-Nasser ; Heba Kashef
  • 期刊名称:International Journal of Interactive Multimedia and Artificial Intelligence
  • 印刷版ISSN:1989-1660
  • 出版年度:2021
  • 卷号:6
  • 期号:4
  • 页码:157-163
  • DOI:10.9781/ijimai.2020.08.002
  • 语种:English
  • 出版社:ImaI-Software
  • 摘要: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.
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