Modeling and characterization of a grid-connected photovoltaic system under tropical climate conditions

Aaref, Ashty M.; Hamada, Hussein M.; Sulaiman, Mohd Herwan; Amin, Nowshad; Mekhilef, Saad

doi:10.1016/j.rser.2017.08.053

Cited by 25 publications

(11 citation statements)

References 57 publications

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“…With just under 5 hours sun per day, it successfully charges the devices devices with 92% efficiency. This is in line with the average efficiency of an inverter being 95.15% [12]. Through the optimum placement relative to the sun the Solar Tree will prove to be indispensable and could easily generate electricity during 12 hours of daylight.…”

Section: Resultssupporting

confidence: 53%

Solar Tree

Ahmad¹

2018

JESR

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In this day and age, with the ever-growing population and energy demand, we should take the renewable option route in our energy source. We should also keep in mind that said energy should not cause any lasting environmental damage, one of the perfect example being solar energy. A country that is hot and sunny all year long is the perfect contributor to solar energy, case in point, Malaysia. With that in mind Solar Tree is designed and developed to facilitate consumers who need electric power at any place, anytime, anywhere. The objective of this study is to assess a mini project in the likes of Solar Tree that can generate electricity without harming the environment, despite the weather. Intended specifically to be a mini project, it is understandable that electricity generated is limited, with only up to 500W in total. As a trial, two electronic devices were tested, specifically a mobile phone and a laptop, as both devices are used almost every day. The data collected is then tabulated and analysed. It was concluded the solar tree developed proved efficient in charging both devices and will continue to do so given enough sunlight.

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Section: Resultssupporting

confidence: 53%

Solar Tree

Ahmad¹

2018

JESR

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“…The evaluation is going on all data points which are considered in the regression analysis. This evaluation is used to guarantee the accuracy of the model using the real data [59]. Many references used the standard error analysis like the root mean squared error (RMSE) and mean absolute error (MAE) to determine the accuracy of their methods [24].…”

Section: Methodsmentioning

confidence: 99%

Optimal parameters estimation and modelling of photovoltaic modules using analytical method

Elkholy

El‐Ela

2019

Heliyon

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This paper introduces a proposed approach to estimate the optimal parameters of the photovoltaic (PV) modules using in-field outdoor measurements and manufacturers' datasheet as well as employing the nonlinear least-squares fitting algorithm. The main goal is to determine the optimal parameter values of the implemented model which are: series resistance, reverse saturation current, photocurrent, ideality factor and shunt resistance in case of the five parameters model. A Microsoft Excel spreadsheet is developed in order to perform modeling and analysis of the parameters analytical initial values using manufacturer datasheet specifications regarding to the changing in solar irradiance and ambient temperature. Then, the sum of the squared residuals between in-field measured and simulated data are calculated and minimized using Excel solver in order to obtain the optimal values of the parameters simultaneously, to describe the best fit for the outdoor measured data. The proposed approach is used to find the optimal parameters of the PV module TRINA TSM-295 using an array tester. The convergence confidences of the estimated parameters are presented and assessed in an easy way. This approach allows all parameters to be optimized, simultaneously. The results are verified and compared with other research studies for different PV cell technologies. The obtained results are useful for the tested PV module manufacturer and assess the performance of the products in different weather conditions.

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“…However, solar energy has begun to compete for a convenient location as a source of clean energy and a pillar of sustainable development in many countries of the world 2 . The use of solar energy has been promoted in various applications, 3,4 including the thermal heating of water for domestic and industrial purposes, 5 the heating of air for comfort conditions, 6 ventilation using the Trombe wall, 7 and solar distillation 8,9 . In the production of electricity, there are several distinctive and suitable methods for the production of electric power such as the solar chimney, 10 concentrated power stations, 11 and photovoltaic (PV) 12‐14 …”

Section: Introductionmentioning

confidence: 99%

Evaluation ofPVoutput in terms of environmental impact based on mathematical and artificial neural network models

Kazem

2020

Int J Energy Res

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This paper presents different prediction models for a grid-connected photovoltaic (GCPV) system based on long-term data sets. A 1.4 kW GCPV installed in Sohar, Oman, where measurements of the electrical and environmental parameters are taken every second for 5 years (from May 2014 to April 2019) to reduce uncertainty and improve the proposed model's accuracy. The highest power and energy measured from the GCPV are 1180 W and 245.8 kWh, respectively. Mathematical regression and cascade-feed forward propagation (CFNN) models for PV current, voltage, and power were proposed in terms of environmental impact. A NeuroSolutions package was used to propose GCPV models for electrical quantities. An evaluation criterion is defined, in this study, to assess the system's performance. The proposed models show excellent agreement with the measured experimental data. However, CFNN shows a superior accuracy compared with empirical and regression proposed models. The two models have proposed a dispute, but the model relating the solar irradiation and ambient temperature to GCPV current is more accurate. To evaluate and validate the proposed CFNN models, mean square error, MAE, root mean square error, and R 2 metrics have been used as a criterion and compared with different artificial neural networks models in the literature. The proposed CFNN is found to give the most accurate results in terms of MSE = 0.0007, MAE = 0.4310, and RMSE = 0.0290 and highest accuracy R 2 = 0.9999, which shows the effectiveness and feasibility of the proposed models.

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Modeling and characterization of a grid-connected photovoltaic system under tropical climate conditions

Cited by 25 publications

References 57 publications

Solar Tree

Solar Tree

Optimal parameters estimation and modelling of photovoltaic modules using analytical method

Evaluation ofPVoutput in terms of environmental impact based on mathematical and artificial neural network models

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