Solar Power Prediction Modeling Based on Artificial Neural Networks under Partial Shading

Lee, Younghyun; Lee, Jonghwan

doi:10.3390/app131810013

Cited by 2 publications

(1 citation statement)

References 22 publications

(35 reference statements)

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“…This condition, which is referred as partial shading, increases the power loss dramatically and can result in other serious problems including hotspots [5,6]. In order to avoid the problems caused by partial shading, by-pass diodes are used in parallel with solar cells that create a current path to by-pass the shaded cells [7][8][9]. Using a single diode for every cell is not economical and so, usually, a diode is used for several solar cells.…”

Section: Introductionmentioning

confidence: 99%

A novel algorithm for maximum power point tracking using computer vision (CVMPPT)

Ahmadi,

Abrari,

Ghanaatshoar

et al. 2024

PLoS ONE

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The behavior of an illuminated solar module can be characterized by its power-voltage curve. Tracking the peak of this curve is essential to harvest the maximum power by the module. The position of the peak varies with temperature and irradiance and needs to be traced. Under partial shading conditions, the number of peaks increases and makes it more difficult to find the global maximum power point (MPP). Various methods are used for maximum power point tracking (MPPT) that are based on iterations. These methods are time-consuming and fail to work satisfactorily under rapidly changing environmental conditions. In this paper, a novel algorithm is proposed that for the first time, utilizes computer vision to find the global maximum power point. This algorithm, which is implemented in Matlab/Simulink, is free of voltage iterations and gives the real-time data for the maximum power point. The proposed algorithm increases the speed and the reliability of the MPP tracking via replacing analogue electronics calculations by digital means. The validity of the algorithm is experimentally verified.

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Section: Introductionmentioning

confidence: 99%

A novel algorithm for maximum power point tracking using computer vision (CVMPPT)

Ahmadi,

Abrari,

Ghanaatshoar

et al. 2024

PLoS ONE

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Assessing the performance of a monocrystalline solar panel under different tropical climatic conditions in Cameroon using artificial neural network

Dongmo,

Arreyndip,

Tendong

et al. 2024

Journal of Renewable and Sustainable Energy

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To implement the European Union (EU)-Africa Green Energy Initiative in Cameroon to boost the renewable energy sector, we model the performance of a 500 W monocrystalline solar panel in major cities of Cameroon located in different climatic zones to select the best location for the installation of a solar farm. We also evaluate the contribution of seasonal and weather variability to the amount and stability of power generated by the panel using the artificial neural network (ANN). The ANN model was used to train and test the ERA5 hourly data for Bamenda. The model was then used to estimate Photovoltaic (PV) output in Douala, Yaounde, Ngaoundere, Garoua, and Maroua with a mean absolute error of 4.109 × 10−5, 4.699 × 10−5, 3.563 × 10−5, 3.106 × 10−5, and 3.083 × 10−5 kW, respectively. The results show that the ANN can capture the influence of weather variability on the generated output power. Cloud cover and rainfall are found to negatively affect the amount and stability of generated power in the lower latitude cities of Douala and Yaounde compared to the northern cities, with these effects being stronger in the rainy season than in the dry season. Garoua followed by Maroua are proving to be the best locations for installing a solar park in terms of the amount and stability of electricity generated throughout the year. The Cameroonian government, its EU partners, and other stakeholders involved in the development of solar energy in the country will be able to use the results of this study for better decision-making.

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Solar Power Prediction Modeling Based on Artificial Neural Networks under Partial Shading

Cited by 2 publications

References 22 publications

A novel algorithm for maximum power point tracking using computer vision (CVMPPT)

A novel algorithm for maximum power point tracking using computer vision (CVMPPT)

Assessing the performance of a monocrystalline solar panel under different tropical climatic conditions in Cameroon using artificial neural network

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