Robust integral backstepping controllers for boost converter and H-bridge inverter with LC-filter in Photovoltaic systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it