Multi-Objective Approach for Hydro Governors Control Tuning
Bibliographic record
Abstract
Electric power generation in Albania has been almost totally dependent on hydropower since the system inception. In the last decade, although modest, there has been an increased share of power produced from Renewable Energy Sources (RES), mostly from Solar Power Plants. Looking forward, the Albanian government has considered the promotion of Renewable Energy as an important part of energy policies. Some of the future targets are to increase more the share of injected Renewable Energy in terms of hydro, Photovoltaic (PV) and Wind. Wind Energy is one of the most discussed sources regarding renewable energy penetration. Starting from feasibility studies to the quality of power, there are many issues to be addressed before installations of Wind Energy can be considered. This paper gives an overview of the studies performed regarding System Frequency Response (SFR) by replacing hydro inertia with non-synchronous generation. The paper presents a study of frequency behavior under several penetration levels of non-synchronous generation. Also presented are the results of study cases which show how system frequency is affected when replacing hydro-power inertia with wind turbine converter inertia.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".