Impact of Inductance and Capacitance on MPPT Charge Controller Performance across Various Locations
Bibliographic record
Abstract
The transition toward sustainable energy systems has intensified the demand for more efficient solar power technologies, with Maximum Power Point Tracking (MPPT) charge controllers serving as a critical component for improving photovoltaic performance. However, the role of passive elements such as inductance and capacitance in shaping MPPT efficiency under diverse environmental conditions remains underexplored. In this study, MATLAB Simulink models were developed to investigate the impact of inductive and capacitive configurations on MPPT controller performance across two geographically distinct regions: St. John’s, Newfoundland, and Kaptai, Chittagong. Multiple design scenarios were simulated to evaluate system adaptability and output stability under varying solar irradiance and temperature profiles. The results demonstrate that optimized component sizing significantly enhances energy extraction, while inappropriate tuning leads to sharp declines in system efficiency. To assess long-term dependability, a reliability analysis was also conducted, supported by probability density and cumulative distribution function evaluations. The findings indicate that the proposed configurations exhibit high operational stability and strong reliability metrics, ensuring consistent performance over extended lifespans. This study highlights the necessity of location-specific tuning of inductance and capacitance in MPPT design. By providing a quantified understanding of their influence on controller dynamics, the study offers practical insights for developing robust, efficient, and reliable solar energy systems tailored to diverse climatic contexts.
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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 | 0.001 |
| 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) | 0.001 | 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 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".