Design and Real-Time Validation of Higher Order Sliding Mode Observer-Based Integral Sliding Mode MPPT Control for a DC Microgrid
Bibliographic record
Abstract
In a photovoltaic (PV) system-based microgrid, maximum power point tracking (MPPT) control plays a crucial role to improve the efficiency and stability. Since the past few years, one of the key control schemes to enhance the effectiveness of the microgrid is the observer-based MPPT control. This article proposes a higher order sliding mode observer (HOSMO)-based integral sliding mode control (ISMC) for MPPT control to ensure an efficient operation of a closed-loop dc microgrid. The proposed MPPT control is mainly focused on obtaining a chatter-free output voltage and stabilized output power from the PV-system-based microgrid and further ensure insensitivity to uncertainties and reduction in steady-state error. ISMC is applied to carry out finite-time stabilization throughout the entire response of the system. To justify the efficacy of the proposed approach, various test scenarios are simulated in real-time, and the performance is investigated through extensive comparative results. The MATLAB simulations and real-time simulation results achieved with OPAL-RT are compared. The superior performance of the proposed approach is observed in terms of high efficiency, good accuracy, and robust performance under varying meteorological conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".