Hybrid Variable-Structure Control With Evolutionary Optimum-Tuning Algorithm for Fast Grid-Voltage Regulation Using Inverter-Based Distributed Generation
Bibliographic record
Abstract
Fast grid-voltage regulation is a necessary requirement in a power distribution system, particularly in feeders serving voltage-sensitive loads. Severe and random voltage disturbances might be initiated by time-varying loads, nondispatchable generation, voltage transients associated with parallel connected loads, and voltage transients caused by capacitor switching. These voltage disturbances are stochastic in nature, with durations vary from a fraction of a cycle to few cycles. To ensure perfect regulation of the voltage at the point of common coupling (PCC) and provide means for rejecting voltage disturbances, the voltage control loop should offer a high disturbance rejection performance. This paper presents a newly designed grid-voltage control scheme, for the distributed generation interface, based on a hybrid linear with variable-structure control voltage controller. The proposed voltage controller can embed a wide band of frequency modes through an equivalent internal model. Subsequently, wide range of voltage perturbations, including capacitor-switching voltage disturbances, can be rejected. To optimally tune the proposed nonlinear voltage controller, the tuning problem is formulated as a constrained optimization problem, and solved via an evolutionary search algorithm based on the particle-swarm-optimization (PSO) technique. Therefore, a simple and structured tuning methodology can be obtained. To provide accurate and robust tracking of the generated reference current trajectory, a newly designed robust deadbeat current control algorithm is adopted. Theoretical analysis and comparative evaluation tests are presented to demonstrate the effectiveness of the proposed control scheme.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".