An intelligent method for sizing optimization in grid-connected photovoltaic system
An intelligent method for sizing optimization in grid-connected photovoltaic system
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DOI：
10.1016/j.solener.2012.04.009
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发表时间：
2012-07
期刊：
影响因子：
6.7
通讯作者：
S. Sulaiman;T. Rahman;I. Musirin;S. Shaari;K. Sopian
中科院分区：
文献类型：
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作者：
S. Sulaiman;T. Rahman;I. Musirin;S. Shaari;K. Sopian
提出了一种基于进化规划(EP)的光伏并网发电系统智能调整方法。利用EP为系统选择最优的光伏组件和逆变器，使系统的技术或经济性能达到最优。优化过程的决策变量是光伏组件和逆变器，它们在各自的数据库中被编码为特定的整数。另一方面，将优化任务的目标函数设定为系统的技术性能或经济性能均为最优。在实施基于智能的大小调整算法之前，提出了一个传统的大小调整模型，该模型后来导致了基于迭代的大小调整算法的开发，称为ISA。由于ISA测试了要考虑用于该系统的所有可用光伏组件和逆变器组合，整个规模调整过程变得既耗时又乏味。因此，提出了基于EP的大小调整算法，称为EPSA，以加快大小调整过程。在EPSA的开发过程中，对不同的EP模型进行了测试，并引入了一个非线性比例因子来改善这些模型的性能。结果表明，EPSA在计算时间上优于ISA。此外，非线性比例因子的引入也改善了所研究的所有EP模型的性能。此外，与使用不同类型计算智能的其他基于智能的大小调整算法相比，EPSA也表现出了最好的优化性能。
This paper presents an intelligent sizing technique for sizing grid-connected photovoltaic (GCPV) system using evolutionary programming (EP). EP was used to select the optimal set of photovoltaic (PV) module and inverter for the system such that the technical or economic performance of the system could be optimized. The decision variables for the optimization process are the PV module and inverter which had been encoded as specific integers in the respective database. On the other hand, the objective function of the optimization task was set to be either to optimize the technical performance or the economic performance of the system. Before implementing the intelligent-based sizing algorithm, a conventional sizing model had been presented which later led to the development of an iterative-based sizing algorithm, known as ISA. As the ISA tested all available combinations of PV modules and inverters to be considered for the system, the overall sizing process became time consuming and tedious. Therefore, the proposed EP-based sizing algorithm, known as EPSA, was developed to accelerate the sizing process. During the development of EPSA, different EP models had been tested with a non-linear scaling factor being introduced to improve the performance of these models. Results showed that the EPSA had outperformed ISA in terms of producing lower computation time. Besides that, the incorporation of non-linear scaling factor had also improved the performance of all EP models under investigation. In addition, EPSA had also shown the best optimization performance when compared with other intelligent-based sizing algorithms using different types of Computational Intelligence.