دانلود مقاله ISI انگلیسی شماره 78887
عنوان فارسی مقاله

الگوریتم های تکاملی چند هدفه برای طراحی سیستم های ردیابی خورشیدی متصل به شبکه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
78887 2014 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Multi-objective evolutionary algorithms for the design of grid-connected solar tracking systems
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Electrical Power & Energy Systems, Volume 61, October 2014, Pages 371–379

کلمات کلیدی
گیاهان فتوولتائیک؛ بهینه سازی عددی؛ الگوریتم های تکاملی چند هدفه؛ انرژی تجدید پذیر
پیش نمایش مقاله
پیش نمایش مقاله الگوریتم های تکاملی چند هدفه برای طراحی سیستم های ردیابی خورشیدی متصل به شبکه

چکیده انگلیسی

The decentralization of electrical power production is conducive to a more effective and harmonious use of energy resources. For this reason, photovoltaic grid-connected plants (PVGCPs) as well as other renewable energy sources have come into the spotlight in recent years since they improve the supply of electrical power to the grid. The optimization of PVGCP design has been previously addressed in terms of electrical losses with successful results. However, PVGCP performance can be further enhanced if other characteristics, such as power capacity, are taken into consideration. This paper focuses on the optimization of the design of photovoltaic plants with solar tracking. The research described had the following two objectives: (i) the maximization of power capacity; (ii) the minimization of electrical losses. This problem was solved with multi-objective evolutionary algorithms, which have proved to be powerful optimization techniques that are useful for a wide range of objectives. This paper focuses on the NSGA-II and SPEA2, two well-known multi-objective algorithms, and describes how they were used to optimize PVGCPs. The resulting sets of solutions provide the flexibility and adaptability needed to build a PVGCP. These algorithms were thus found to be an effective tool for enhancing PVGCP performance.

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