Sag and Flicker Reduction Using Hysteresis-Fuzzy Control-Based SMES Unit
Bibliographic record
Abstract
The issue of power quality is one of the prominent factors in determining the reliability of power systems. It is inevitable to consider power quality issues and include appropriate mitigation techniques in the design of any future power system project construction. Voltage sag and flicker are the two most common short disturbance types of power quality issues that frequently occur in power systems. A widely accepted mitigation strategy to enhance the power quality of disturbed networks is by connecting a flexible ac transmission system. In this paper, a superconducting magnetic energy storage (SMES) unit along with hysteresis-fuzzy controller to suppress the severe impacts of voltage sag and flicker events on the power quality of a power system is proposed, implemented, and tested. The proposed SMES configuration with hysteresis current control and fuzzy logic controller is effective, simple, and easy to implement. Simulation results show the ability of the proposed SMES controller in compensating sag and/or flicker distortions.
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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) | 0.000 | 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 | 0.000 |
| 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".