Case Studies for Supplying the Alternating Current Auxiliary Systems of Substations with a Voltage Equal to or Higher than 230 kV
Bibliographic record
Abstract
This paper presents case studies for replacing diesel generators (DGs) that are used as the main and emergency power sources for alternating current (AC) auxiliary services in substations (SS) within the transmission network (voltage greater than or equal to 230 kV). The objective of this research is to present a solution that is more reliable, environmentally friendly, and financially viable than DGs. To achieve this, the proposed solutions incorporate Battery Energy Storage Systems (BESSs) with or without the integration of Photovoltaic (PV) Systems. These solutions were simulated using the HOMER PRO Version 3.14.5 software for the Messias SS /AL, and the results were analyzed and compared to the DG in terms of reliability, financial viability, and environmental impact. Based on the conducted analyses, the BESS solution with the PV system was found to be the most suitable for the main source. However, in the case of the emergency source, if one of the main sources is a DG/BESS, maintaining the emergency DG is the preferable option. If both main sources are independent, the BESS solution with the PV system is a suitable solution.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".