کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7157748 1462788 2018 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Parameter identification of solid oxide fuel cells with ranking teaching-learning based algorithm
ترجمه فارسی عنوان
شناسایی پارامترهای سلول های سوخت اکسید جامد با الگوریتم مبتنی بر یادگیری مبتنی بر یادگیری
کلمات کلیدی
سلول سوختی اکسید جامد، شناسایی پارامتر، الگوریتم آموزش مبتنی بر یادگیری، مکانیزم رتبه بندی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی (عمومی)
چکیده انگلیسی
The performance of a solid oxide fuel cell (SOFC) is tightly related to relevant parameters associated with the internal multi-physicochemical processes. Accurate identification of these parameters is considerably important for modelling the voltage versus current (V-I) characteristic of SOFCs. In this paper, an improved teaching-learning based algorithm (TLBO) referred to as RTLBO is proposed to identify the exact values for these parameters. The parameter identification of SOFCs is transformed into a minimization optimization problem. The mean square error (MSE) between the measured output voltage and the calculated output voltage is used as the objective function. TLBO has been shown to be competitive with other population-based algorithms. However, its convergence rate is relatively slow especially for complex optimization problems. Inspired by the ranking mechanism in the actual scenarios of teaching-learning process, a ranking based learner selection method is proposed and integrated into both the teacher and learner phases of RTLBO. In RTLBO, poor learners are more likely to be eliminated from the current class in the ranking based teacher phase and good learners are more likely to be chosen to interact with others in the ranking based learner phase, which hence can improve the overall performance of the class quickly. The experimental results on a 5-kW SOFC stack comprehensively demonstrate that RTLBO is able to achieve a better trade-off between the exploration and exploitation compared with twelve advanced TLBO variants and eight popular advanced non-TLBO based methods. In addition, the sensitivity of RTLBO to variations of population size is empirically investigated.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy Conversion and Management - Volume 174, 15 October 2018, Pages 126-137
نویسندگان
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