Design and Development of DC-DC Converters Based on IoT Systems for Photovoltaic Applications in Egypt
Bibliographic record
Abstract
Renewable energy sources are naturally pollution-free, and solar energy meets the world's energy demand. Photovoltaic (PV) systems are used to harvest solar energy, where a power converter is needed to regulate and control the harvested solar power and achieve the required output voltage for various applications. In this paper, four different DC-DC converters commonly used in research and industry are designed and developed to provide the required voltage of the PV systems, which are Buck, Synchronous Buck, Single-Ended Primary Inductor Converter (SEPIC), and Flyback converters. These DC-DC converters are modeled, simulated, and analyzed using LTspice software. The simulation studies and analysis quantify the system performance of these converters and the parameter values for the experiments. These converters are built and tested in different conditions using a standalone test bench to validate these converters for PV application systems. The test development sequences are conducted for open-loop and closed-loop systems of the power converters, where a variable power supply is used to mimic the solar panel output in the test bench. A proportional integral (PI) controller is designed to regulate the output voltage of the power converters to power load or charge the battery while solar radiation level, load, and other variables change. The Experimental test results show that the system performance profiles of these converters are different in ripple, efficiency, and other values. The maximum efficiency value achieved by the Synchronous Buck converter and less ripple output voltage is from the Flyback converter. Finally, the best proposed DC-DC converter is connected to a solar panel, where a customized Internet of Things (IoT) system is deployed into the PV system to supervise and monitor the parameters of the proposed PV system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".