Silicon Photovoltaic Systems Performance Assessment Using the Principal Component Analysis Technique. (2022)

• Record Type: Journal Article
• Authors: Lotfi, Hicham; Adar, Mustapha; Bennouna, Amin; Izbaim, Driss; Oum'bark, Fatimazahra; Ouacha, EL Houssaine
• Is Part Of: Materials today. Volume 51:Part 6(2022)
• Page Start: 1966
• Page End: 1974
• Publication Date: 2022
• Subjects: AC Alternative Current -- DC Direct Current -- p Peak -- STC Standard Test Conditions -- TaAmbient Temperature, °C -- PCA Principal Component Analysis -- CF Capacity Factor % -- PR Performance Ratio % -- PV Photovoltaic; Efficiency system -- Performance ratio -- Final yield -- Principal Component Analysis; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.04.374
• Languages: English
• ISSNs: 2214-7853

Abstract

Highlights: We collected two years of energy output of three different PV systems. The performance parameters are used to evaluate the proposed system. Principal Component Analysis is used to study the correlation between the performance parameters and the meteorological variables Abstract: The aim of this paper is to further deepen the use of a very robust mathematical technique in the analysis of data. The Principal Component Analysis (PCA) method is used to analyze the performance of three PV systems and to determine the correlation between performance parameters and meteorological variables. The three systems are based on Silicon monocrystalline (2 kWp), polycrystalline (1.82 kWp) and amorphous (1.55 kWp) PV modules. The performance indicators used are the parameters described in the international standard IEC 61724. The evaluation of parameters such as performance ratio, final yield and capacity factor were found to be 79.08%, 1876.48kWh/kWp/year and 38.46% for polycrystalline, 71.13%, 1688.97kWh/kWp/year and 38.99% for monocrystalline and 78.89%, 1866.42kWh/kWp/year and 33.02% for amorphous, respectively. The projection of the data showed that more than 87% of the information is retained and presented in 2D space. The correlation between temperature and performance of mc-Si, pc-Si, and a-Si systems is −21%, −12.5% and −42.8%, respectively.