Optimal Sizing of Thyristor-Controlled Impedance for Smart Grids With Multiple Configurations
Bibliographic record
Abstract
Smart grids have become one of the important and challenging topics due to the numerous benefits it can bring to the power system. In this context, distributed generation (DG) is expected to play a significant role. The smart grid can have multiple configurations depending on the smart grid operating strategy and system conditions. In smart grids, DG could be operated either grid connected or islanded. Such flexible and variable configuration results in variable fault current levels which could impact the operation of the existing protective devices on the distribution system. In this paper, it is proposed to optimally size thyristor-controlled impedance (TCI) of both inductive and capacitive type to manage the fault current levels under different smart grid configurations. The salient benefit is to avoid damage and delayed operation of protective devices due to the variability in fault currents with synchronous-based DG. The problem is formulated as a nonlinear programming (NLP) problem and the optimum size and type of the TCI is determined using particle swarm optimization (PSO). Results show that by optimally locating and sizing TCI, fault current levels under various smart grid configurations can be managed and thus avoiding protective device coordination failure and damage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".