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. 2022 Dec 12;8(12):e12237.
doi: 10.1016/j.heliyon.2022.e12237. eCollection 2022 Dec.

An improved model and performance analysis for grid-connected photovoltaic system in Oman

Omar A Al-Shahri  1 , Firas B Ismail  1 , Nizar F O Al-Muhsen  2 , Ammar Al-Bazi  3 , M A Hannan  4
Affiliations

An improved model and performance analysis for grid-connected photovoltaic system in Oman

Omar A Al-Shahri et al. Heliyon. .

Abstract

The PV systems' sources are environmentally friendly, but at the same time, they are constantly changing with time. When evaluating solar energy resources, it is necessary to consider the variability and effects of different environmental operation parameters like solar irradiances, ambient temperature, and module temperature. The study introduces a method to simulate an existing photovoltaic system using a mathematical model that permits intelligent strategies to optimise the efficiency and adjust the most effective operational parameters for the solar energy systems. A mathematical analysis for the data framework, including correlation and regression coefficients, was calculated to identify and chart the relationships between the system's most influential parameters and the generated power from the PV system. An improved mathematical model was built with the most influential parameters. The improved model was simple, accurate, and based on the loss ratio by eliminating the unknown parameters. The system's efficiency was analysed using an existing data framework-recorded hourly from 1st January 2017 to December 2018 for a grid-connected photovoltaic system installed in the south of Oman. The results showed that the most influential parameters on the efficiency were the module's solar irradiance and surface temperature. The operating parameters such as ambient temperature, wind speed, and air humidity had a negligible effect on the generated power compared to the cell temperatures and solar radiation. The dissipation factor was used in the new output current and voltage equations to stimulate the output power of the PV model. The improved model was validated in a MATLAB Simulink and showed a more promising output with a lower RMSE of 5 %.

Keywords: Data analysis; Energy; Mathematical model; Solar energy systems; System efficiency analysis; Validation of the established model.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Performance of the PV panel during its life span.
Figure 2
Figure 2
Flow chart for the developed Mathematical model.
Figure 3
Figure 3
Mathematical model output using equations [26, 27, 28] for one PV cell.
Figure 4
Figure 4
Performance of the PV mathematical model with actual data in 2017.
Figure 5
Figure 5
Performance of the PV mathematical model with actual data in 2018.
Figure 6
Figure 6
The location of the PV power station.
Figure 7
Figure 7
Monthly generated power in 2017.
Figure 8
Figure 8
Monthly performance ratio in 2017.
Figure 9
Figure 9
Monthly generated power in 2018.
Figure 10
Figure 10
Monthly performance ratio in 2018.
Figure 11
Figure 11
Temperature loss coefficients in 2017 and 2018.
Figure 12
Figure 12
Correlation between the generated power and the outdoor parameters in winter 2017.
Figure 13
Figure 13
Correlation between the generated power and the outdoor parameters in summer 2017.
Figure 14
Figure 14
Correlation between the generated power and the outdoor parameters in winter 2017–2018.
Figure 15
Figure 15
Correlation between the generated power and the outdoor parameters in summer 2018.
Figure 16
Figure 16
Correlation between the generated power and the outdoor parameters in winter 2018.
Figure 17
Figure 17
Regression lines for the power and PV temperatures in different seasons from 2017 and 2018.
Figure 18
Figure 18
Regression lines for the power and solar irradiances in different seasons from 2017 and 2018.
Figure 19
Figure 19
The R square obtained from the regression analysis.
See this image and copyright information in PMC

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