System Level-Based Voltage-Sag Mitigation Using Distributed Energy Resources
Bibliographic record
Abstract
As the growing demand for battery energy storage systems (BESSs) generally follows the renewable energy sources (RESs) trend, the active management of BESS- and RES-based distributed energy resources (DERs) could effectively solve voltage-sag problems in active distribution systems (ADSs). DERs are typically interfaced with the grid through power-electronic inverters. They are considered power resources with adjustable active/reactive power injections that could contribute to the enhancement of the grid's performance. This article proposes a new framework for active/reactive power management of inverter-based DERs to mitigate voltage-sag problems in ADSs. The new framework utilizes a novel zonal-based voltage control algorithm for mitigating voltage-sag problems in ADSs. The algorithm identifies mitigation zones' virtual boundaries based on a proposed electrical influence intensity index that reflects the electrical connectivity of the network. A novel zone capability index (ZCI) ranks the mitigation zones according to DER capacity, zonal loading conditions, and the number of nodes within each mitigation zone. The voltage mitigation algorithm minimizes the energy injected from DERs’ inverters to achieve maximum voltage sag alleviation in each mitigation zone. This zonal-based mitigation algorithm is applied to the IEEE 33-bus distribution system under voltage-sag conditions to verify the proposed algorithm's feasibility and effectiveness.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".