Modelling and Performance Evaluation of the Virtual Air Gap Variable Reactor
Bibliographic record
Abstract
This thesis describes a novel device, the Virtual Air Gap Variable Reactor (VAG-VR), which is capable of producing a continuously \nvariable reactance by locally saturating a small section of the reactor core via an embedded dc control winding. Variable Reactors have many applications in the power industry such as control of line power flow, voltage regulation, reactive line compensation and limiting inrush currents. A variable reactor is most commonly implemented as a thyristor controlled reactor (TCR) by switching in and out a constant reactance to achieve an averaged variable reactance. By using a virtual air gap, a continuously variable reactance is possible. The VAG-VR offers a better dynamic response, without introducing the harmonics created by the thyristor switching of a TCR. The VAG-VR gives low triplen harmonics and therefore allows control of reactive power in single phase or unbalanced three phase systems as would be required in the distribution system. \n \n \nAn experimental prototype VAG-VR was developed to investigate three main performance measures: steady state performance, dynamic response and harmonic performance. Over the operating range of the VAG-VR inductance was varied from 100% to 9% of its original value. The dynamic response of the VAG-VR is approximately one tenth of a cycle. This compares favorably to a TCR which responds in approximately half a cycle. Harmonics are also shown to be significantly reduced in the VAG-VR compared to the TCR. \n \n \nA dynamic model of the VAG-VR, suitable for incorporation into power system simulations, was developed and validated. Parameters were determined both experimentally and through finite element method (FEM) simulations. Both experimental and simulation results indicate that the VAG-VR offers a technically viable alternative to the TCR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".