Real-Time Fuzzy Voltage Regulation for Distribution Networks Incorporating High Penetration of Renewable Sources
Bibliographic record
Abstract
This paper proposes a coordinated voltage regulation scheme for on-load tap changer (OLTC) and renewable distributed generation (DG) units to provide a proper voltage regulation for active distribution systems. The main motivation of applying fuzzy logic is that it can deal with environments of imperfect information, and thus, it can reduce communication requirements. The proposed regulation scheme consists of three fuzzy-based control algorithms. The first control algorithm is proposed for the OLTC such that it can mitigate the effect of DG units on the voltage profile. The second control algorithm is proposed to provide a DG reactive power sharing, in order to relax the OLTC tap operation. The third control algorithm aims to partially curtail DG active powers to restore a feasible solution from the OLTC prospective. The proposed fuzzy algorithms have the advantage of providing proper voltage regulation with relaxed tap operation, utilizing only the estimated system minimum and maximum voltages. Moreover, it avoids numerical instability and convergence problems associated with centralized approaches, as it does not require to run an optimization algorithm. Real-time simulations are developed to show the effectiveness of the proposed fuzzy algorithms on a typical distribution network, using OPAL-RT real-time simulator.
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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 | 0.001 | 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".