ارسل ملاحظاتك

ارسل ملاحظاتك لنا







الرئيسية > Using The Bees Algorithm To Se...>الوصف

Using The Bees Algorithm To Select The Optimal Speed Parameters For Wind Turbine Generators

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Fahmy, A. A. (Author)
المجلد/العدد: مج24, ع1
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2012
الصفحات: 17 - 26
DOI: 10.33948/0584-024-001-003
ISSN: 1319-1578
رقم MD: 972028
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
علوم الحاسوب | خوارزمية النحل | توليد الطاقة | طاقة الرياح
كلمات المؤلف المفتاحية:
Optimization | Wind Turbine Generator | Capacity Factor | Bees Algorithm | PSO | Swarm Intelligence
رابط المحتوى:
PDF (صورة)     
صورة الغلاف QR قانون
حفظ في:
المستخلص: The Bees Algorithm is a recently developed optimization technique that mimics the foraging behavior of honey bees in nature. This study investigates the use of the Bees Algorithm for the selection of the optimal operating speed parameters for wind power units. Three speed parameters need to be optimized, namely, the rated, cut-in, and cut-off (furling) speed of the turbine. The aim of the optimization process is to maximize the yearly power yield and turbine usage time. The choice of the best parameters depends from the wind frequency distribution at the site of installation. Eleven locations on the coastal areas of Egypt were chosen as case studies. The well-known Particle Swarm Optimization was used as a control optimization algorithm. A popular classical approach based on the manual optimization of the sole rated speed was used as baseline for the comparison of results. The optimization of all the three speed parameters and the use of intelligent optimization techniques represent the novelties of this paper. The study showed that the Bees Algorithm outperformed the other two optimization methods. The proposed algorithm was able to find speed parameters that greatly enhanced the power yield, without compromising the usage time or significantly increasing the capital costs. The comparison between the standard manual optimization method and the two intelligent optimization techniques proved the superiority of the latter ones.
ISSN: 1319-1578

عناصر مشابهة


© 2024 المنظومة. جميع الحقوق محفوظة.