Department of Automation and Control Engineering, Far East University, Tainan City, 744, Taiwan, ROC.
Abstract
In the paper, a cloud-dust based intelligent maximum performance analysis system for power generation with solar energy is proposed. In order to resolve performance problems for power generation using solar energy, factors of the photovoltaic are integrated to the cloud-dust based intelligent maximum power analysis system for computing. This study concerns the development of a maximum performance analysis system for power generation using solar energy, in order to improve the eects of dierent regions on the solar panels and enable them to get maximum eciency of power generation. The design methodology of this study includes: (1) Records and surveillance module; (2) Prediction and assessment module; (3) Performance diagnosis module; and (4) Maintenance prescription module, with which we are able to nd the design and implementation of records, surveillance, prediction, assessment, diagnosis and prescription for power generation with solar energy. It has worked successfully. The advantages of the clouddust based intelligent, maximum performance analysis system for power generation with solar energy include an increase in the overall competitive performance of the products, and a reduction in the cost of the products and use of human resources
Huang, C. (2015). Study on cloud-dust based intelligent maximum performance analysis system for power generation with solar energy. Scientia Iranica, 22(6), 2170-2177.
MLA
C.-C. Huang. "Study on cloud-dust based intelligent maximum performance analysis system for power generation with solar energy". Scientia Iranica, 22, 6, 2015, 2170-2177.
HARVARD
Huang, C. (2015). 'Study on cloud-dust based intelligent maximum performance analysis system for power generation with solar energy', Scientia Iranica, 22(6), pp. 2170-2177.
VANCOUVER
Huang, C. Study on cloud-dust based intelligent maximum performance analysis system for power generation with solar energy. Scientia Iranica, 2015; 22(6): 2170-2177.