應用改良基因演算法於自然光菱鏡集光器之配置｜國立臺灣科技大學博碩士論文系統

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研究生：	楊政偉 Jheng-wei Yang
論文名稱：	應用改良基因演算法於自然光菱鏡集光器之配置 Prism-based sunlight concentrator layout using a revised genetic algorithm
指導教授：	王孔政 Kung-jeng Wang
口試委員:	周碩彥 Shuo-yan Chou 黃忠偉 Jong-woei Whang
學位類別：	碩士 Master
系所名稱：	管理學院 - 工業管理系 Department of Industrial Management
論文出版年：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	55
中文關鍵詞：	自然光集光器、基因演算法、菱鏡配置
外文關鍵詞：	Sunlight concentrator, genetic algorithm, prisms layout
相關次數：	點閱：233 下載：3
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太陽能是一種對於自然環境能產生少許負面影響的綠色能源。其中菱鏡結構所組成的集光器廣泛地被使用在許多太陽能源的應用上。因此，本研究發展一套以創新菱鏡配置方法的基因演算法來改善集光器的集光效率。此外，本研究也考量到集光器的對稱性因子以發展出具有對稱程度且高集光效率的集光器。並透過設計和改良基因演算法的基因操作子來發展集光器最佳集光效率的配置。我們進行數次實驗來評估不同運算子間對集光效率的表現。實驗結果顯示，單純考量集光最佳化，集光器能提升平均200%~230%的集光效率；而加入對稱性因子的結果顯示，集光器能提升平均200%~215%的集光效率。此外，本研究也透過敏感度分析的實驗來探討基因演算法應用在自然光集光器配置的適應性。

Solar light is a source of green energy and produces less negative impacts on the environment. The layout of a prism-based sunlight concentrator has been comprehensively utilized and a great influence on sunlight concentration system. This study develops a revised genetic algorithm (GA) to improve such layout for improving the sunlight concentration efficiency. In addition to the sunlight concentration, this study considers the layout symmetry as well. Experiments are conducted to evaluate the performance of per guiding fiber by different GA operators. Experiment results show that 200%~230% improvement in brightness and 200%~215% in symmetry is achieved. In addition, sensitivity is conducted to test different sizes of sunlight concentrators.

摘要 I
ABSTRACT II
CONTENT III
FIGURE LIST V
TABLE LIST VII
Chapter 1　Introduction 8
1.1 Research background	8
1.2 Research motivation	9
1.3 Research purpose 9
1.4 Research Structure 10
Chapter 2　Literature Survey 11
2.1 Solar concentrator and optical device design 11
2.2 Exact mathematical model and soft-computing methods	13
2.3 Facility planning and genetic algorithm 14
Chapter 3  A Revised Genetic Algorithm for Sunlight Concentrator 15
3.1 Assumption in PLGA 15
3.2 Prism types	17
3.3 Chromosome design in PLGA 20
3.4 Logic of PLGA 21
3.5 Fitness function 28
3.6 Crossover operators 30
3.7 Mutation operators 34
Chapter 4  Results and Discussions 36
4.1 Sensitivity analysis on GA operators 36
4.2 Optimal sunlight concentrator layout 40
4.3 Sensitivity analysis on problem parameters 42
Chapter 5 Conclusions 46
Chapter 6 Future Research 47
Reference 48
Appendix 51
A. Data of Experiments on Score 51
B. Data of Experiments on Brightness(Lumen) 51
C. User Interface of PLGA 52

                                

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