固定式太阳能复合抛物面聚光光伏集热器系统的模糊集理论不确定性分析

Hoe-Gil Lee, Singiresu S. Rao
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引用次数: 4

摘要

研究了固定式太阳能复合抛物聚光器(CPC)集热器系统与太阳能光伏电池组合使用的不确定性分析。在太阳能CPC PV集热器系统中,任何在集热器接收角内的辐射都通过孔径进入,并通过多次内部反射到达吸收器表面。任何太阳能集热器的设计都必须以最大化PV性能为目标,因为这将引起更高的太阳辐射收集。为了分析太阳能CPC集热器系统优化问题制定中的不确定性,提出了三个目标。考虑季节性需求,以最大化这两个目标,年平均入射太阳能和冬季最低月入射太阳能;CPC收集器系统的最低成本是第三个目标。本文利用模糊集理论对太阳能CPC光伏集热器系统进行了不确定性分析。模糊分析方法适用于模糊问题的变化预测。不确定参数被视为随机变量或不确定输入来预测性能。利用模糊隶属函数对太阳能光伏集热器不确定或不精确的设计参数进行建模。利用三角隶属函数将不确定参数表示为模糊量。采用模糊集分析方法对三个目标约束优化问题进行了分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncertain Analysis of a Stationary Solar Compound Parabolic Concentrator PV Collector System Using Fuzzy Set Theory
The uncertain analysis of fixed solar compound parabolic concentrator (CPC) collector system is investigated for use in combination with solar PV cells. Within solar CPC PV collector systems, any radiation within the collector acceptance angle enters through the aperture and finds its way to the absorber surface by multiple internal reflections. It is essential that the design of any solar collector aims to maximize PV performance since this will elicit a higher collection of solar radiation. In order to analyze uncertainty of the solar CPC collector system in the optimization problem formulation, three objectives are outlined. Seasonal demands are considered for maximizing two of these objectives, the annual average incident solar energy and the lowest month incident solar energy during winter; the lowest cost of the CPC collector system is approached as a third objective. This study investigates uncertain analysis of a solar CPC PV collector system using fuzzy set theory. The fuzzy analysis methodology is suitable for ambiguous problems to predict variations. Uncertain parameters are treated as random variables or uncertain inputs to predict performance. The fuzzy membership functions are used for modeling uncertain or imprecise design parameters of a solar PV collector system. Triangular membership functions are used to represent the uncertain parameters as fuzzy quantities. A fuzzy set analysis methodology is used for analyzing the three objective constrained optimization problems.
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