کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6679752 1428064 2018 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models
ترجمه فارسی عنوان
الگوریتم جستجوی فراخوان چندگانه برای تخمین پارامترهای مدلهای فتوولتائیک
کلمات کلیدی
شناسایی پارامتر، مدل فتوولتائیک، الگوریتم جستجوی عقبگردان، یادگیری چندگانه،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
چکیده انگلیسی
Obtaining appropriate parameters of photovoltaic models based on measured current-voltage data is crucial for the evaluation, control, and optimization of photovoltaic systems. Although many techniques have been developed to solve this problem, it is still challenging to identify the model parameters accurately and reliably. To improve parameters identification of different photovoltaic models, a multiple learning backtracking search algorithm (MLBSA) is proposed in this paper. In MLBSA, some individuals learn from the current population information and historical population information simultaneously, which aims to maintain population diversity and enhance the exploration ability. While other individuals learn from the best individual of current population to improve the convergence speed and thus enhance the exploitation ability. In addition, an elite strategy based on chaotic local search is developed to further refine the quality of current population. The proposed MLBSA is employed to solve the parameters identification problems of different photovoltaic models, i.e., single diode, double diode, and photovoltaic module. Comprehensive experimental results and analyses demonstrate that MLBSA outperforms other state-of-the-art algorithms in terms of accuracy, reliability, and computational efficiency.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 226, 15 September 2018, Pages 408-422
نویسندگان
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