دانلود مقاله انگلیسی رایگان:پیکربندی مجدد تخصیص DG و خازن مبتنی بر شبکه توزیع الکتریکی با استفاده از بهینه ساز اکوسیستم مصنوعی: مطالعه موردی عملی - 2021
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  • Reconfiguration of electrical distribution network-based DG and capacitors allocations using artificial ecosystem optimizer: Practical case study Reconfiguration of electrical distribution network-based DG and capacitors allocations using artificial ecosystem optimizer: Practical case study
    Reconfiguration of electrical distribution network-based DG and capacitors allocations using artificial ecosystem optimizer: Practical case study

    سال انتشار:

    2021


    عنوان انگلیسی مقاله:

    Reconfiguration of electrical distribution network-based DG and capacitors allocations using artificial ecosystem optimizer: Practical case study


    ترجمه فارسی عنوان مقاله:

    پیکربندی مجدد تخصیص DG و خازن مبتنی بر شبکه توزیع الکتریکی با استفاده از بهینه ساز اکوسیستم مصنوعی: مطالعه موردی عملی


    منبع:

    ScienceDirect- Elsevier- Alexandria Engineering Journal, Corrected proof: doi:10:1016/j:aej:2021:11:035


    نویسنده:

    Abdullah Shaheen


    چکیده انگلیسی:

    In this article, a new implementation of Artificial Ecosystem Optimizer (AEO) technique is developed for distributed generators (DGs) and capacitors allocation considering the Reconfiguration of Power Distribution Systems (RPDS). The AEO is inspired from three energy transfer mechanisms involving production, consumption, and decomposition in an ecosystem. In the production mechanism, the production operator allows AEO to produce a new individual randomly, whereas the search space exploration can be improved as illustrated in the consumption mechanism and exploitation can be performed in the decomposition. A practical case study of 59-bus Cairo distribution system in Egypt is simulated with different loading percentages. For optimizing the performance of that practical network, the AEO algorithm is employed for different scenarios. Besides, the results obtained by recent optimization techniques which are Jellyfish Search Optimizer (JFS), Supply Demand Optimizer (SDO), Crow Search Optimizer (CSO), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) are compared with the developed AEO. The simulation results demonstrate the efficacies and superiority of the AEO compared to the others. It surpasses the other algorithms in terms of obtaining the best, mean, worst, and standard deviations. After optimal RPDS and DGs placements, the power losses are decreased by 78.4, 77.84 and 71.4% at low, nominal and high levels, respectively. However, the best scenario with its application prospects is mentioned after optimal RPDS, DGs, and capacitors where the power losses are decreased by 68.8, 85.87 and 89.91% at low, nominal and high levels, respectively.
    KEYWORDS: Artificial ecosystem optimizer | Distributed generators | Electrical systems | Power losses | Reconfiguration


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 14
    حجم فایل: 3718 کیلوبایت

    قیمت: رایگان


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