Optimization of a Hybrid PV-Wind Power System for Enhancing Efficiency and Power Quality Using MATLAB/SIMULINK Simulations
Bibliographic record
Abstract
The increasing reliance on renewable energy has driven the development of hybrid photovoltaic (PV) and wind turbine systems.This study aims to model and simulate a hybrid PV-Wind system using MATLAB/SIMULINK to evaluate its performance and efficiency under varying environmental conditions.The developed model includes PV panels, wind turbines, power converters, and an inverter with Pulse Width Modulation (PWM) control.Simulation results demonstrate that the proposed hybrid system can generate stable power output and effectively adapt to solar irradiance and wind speed fluctuations.The system achieved an overall energy efficiency of 88%, with power quality metrics indicating reduced total harmonic distortion to below 5%.Additionally, implementing an LC filter in the inverter enhances power quality, producing a more sinusoidal AC voltage with a THD of 3.5%.This study confirms that optimized PV-Wind systems provide a reliable and sustainable solution for electricity generation and significantly improve power quality and efficiency in various operational scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".