Finite-Time Convergent Adaptive Sliding Mode Control for Integration of VSCs Into Modernized Microgrids With Parametric and Dynamic Uncertainties
Bibliographic record
Abstract
This research article presents an innovative approach to enhance the performance and robustness of voltage source converters within microgrid systems. Existing controls suffer from slow convergence and unsatisfactory transient performance when dealing with disturbances, especially in the presence of control delay. To discourse these limitations, the proposed sliding mode controller is augmented with a nonlinear disturbance observer to enhance the disturbance elimination capability, attenuates the effect of parameter uncertainties, superior voltage reference tracking and suppressing chattering in the control response. The observer is introduced to estimate sensor values, potentially eliminating the need for dedicated sensors. The proposed control strategy employs a switching Lyapunov function-based mathematical model, addressing dynamic response limitations and switching action incorporating nonlinearities in the proposed model. By incorporating Lyapunov-based stability analysis, the article overcomes limitations and conservatisms associated with traditional linearized techniques, enabling more accurate stability assessments. Online adaptation norms are integrated to estimate and reject external disturbances, aligning closed-loop responses with reference models. Extensive numerical simulations and hardware-in-the-loop experiments validate the improved performance, highlighting the elimination of chattering and enhanced robustness against step and stochastic disturbances.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".