Robust integral backstepping controllers for boost converter and H-bridge inverter with LC-filter in Photovoltaic systems
Bibliographic record
Abstract
Abstract The dependence of photovoltaic system performance on variable weather conditions influences their reliability and efficiency. In order to contribute to solving these problems, a hybrid algorithm combining the basic perturb and observe (P&O) technique and Integral Backstepping Controllers (IBSC) for the control of the boost converter and the single-phase inverter has been proposed and validated using the Matlab/Simulink platform. The proposed control strategies give a good extraction of the photovoltaic Maximum Power Point (MPP) with a DC-DC conversion efficiency of 99.19% and 99.97% for non-linear and linear loads respectively at the solar irradiation of 900 W m−2. The sine wave of the inverter output voltage with a fixed reference has minimized a tracking error to 0 V, while its THD is limited to 0.05% and 0.16% for linear and nonlinear loads, respectively. A comparison of the simulation results with standard Backstepping control, sliding mode control, and hybrid fuzzy-sliding mode control exhibits the effectiveness, superiority, and satisfactory performance of the proposed control schemes in minimizing harmonics under variable irradiance conditions regardless of the load type.
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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 | 0.001 | 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".