Analysis of Heat Potential in Solar Panels for Thermoelectric Generators using ANSYS Software

Harsito, Catur; Triyono, Teguh; Roviyanto, Eki

doi:10.28991/cej-2022-08-07-02

Cited by 27 publications

(6 citation statements)

References 25 publications

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“…AI algorithms play an essential role in establishing ideal operating settings for individual panels by continuously monitoring and evaluating solar irradiance, temperature, and the panels' efficiency. AI increases energy production while avoiding losses due to shading dirt, or panel deterioration, assuring optimal system performance and increased energy output [279], [280]. Accurate energy forecasting is essential for successful grid integration and energy administration in solar power plants.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Harsito et al [33] investigate the heat potential within solar panels for thermoelectric generators, utilizing ANSYS software for comprehensive analysis. The research is anticipated to focus on evaluating the thermal efficiency of solar panels and their capability to produce heat for thermoelectric power generation.…”

Section: Current Research Workmentioning

confidence: 99%

“…The suggested IDT is evaluated for various active power mismatch situations ranging from À20% to +20% (steps of À40%, À20%, 0%, +20%) while keeping the load power constant. The test system is used to assess the likelihood of islanding events for various loadings [33]. During nonislanding scenarios; capacitor bank switching and inductive load switching are taken into account.…”

Section: Proposed (Gbdt-js) Approach For Islanding Detection 41 Data ...mentioning

confidence: 99%

Multiple distributed generators islanding detection using GBDT-JS techniques

Goriparthy,

Balakrishnan

2024

STET

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Photovoltaic arrays, Wind Distributed generators (DG) are progressively utilized as a renewable energy source (RES) in contemporary society. Whenever a DG is detached from the grid, it often results in islanding. In accordance with the standards of the Institute for Electrical and Electronics Engineers, islanding shall be identified within a period of two seconds. This work presents a reliable algorithm-based islanding detection approach to distributed generating systems that utilizes both solar power(SPV) and wind systems.. Gradient Boosting Decision Trees and Jelly Fish Techniques are paired together to create the intelligent algorithm known as GBDT-JS algorithm. The proposed technique focuses on the rate of change in phase angle (RCPP) at the target DG position initially. Islanding and grid disturbances can be assessed by employing discrete wavelet transformations (DWTs).Furthermore, the GBDT-JS method is utilized for the categorization of islands and grid disturbances to determine if the particular system can be adopted or not in terms of various loading and switching capabilities. MATLAB/Simulink platform and Python programming are used to validate the recommended approach

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“…As third generation photovoltaics, the intermediate band solar cells (IBSCs) show extraordinary properties with an ultrahigh conversion efficiency of 63.2%, which far exceeds the Schockly-Queisser limit of 40.7% [ [1] , [2] , [3] , [4] ]. By sequentially absorbing two sub-bandgap photons, electrons in VB can be pumped to the intermediate band (IB) and further transferred to the conduction band (CB).…”

Section: Introductionmentioning

confidence: 99%