Mitigation of FIDVR Using Solid State Transformer in Active Distribution Systems
Bibliographic record
Abstract
The prevalent problem of delayed voltage recovery caused by fault (FIDVR), which leads to extended power restoration after faults and possibly cascading failures, affects the transmission and distribution systems. The FIDVR issue is exacerbated by the growing utilization of air conditioning (A/C) and the significant prevalence of induction motors. A novel approach for improving the (FIDVR) in active distribution networks (ADNs) through the optimized operation of solid-state transformers (SSTs) is presented in this paper. The suggested control strategy employs the untapped potential of SSTs to manage or regulate voltage within ADNs. This method allows SSTs to actively contribute to voltage regulation by managing reactive and active power. To validate the efficiency of this work, the IEEE 13-bus system, including both static and dynamic loads, is implemented using MATLAB/Simulink. The results demonstrate that SSTs can effectively mitigate FIDVR issues, enhancing the resilience and reliability of modern power systems, particularly under conditions of high reactive power absorption.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".