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Record W3108638943 · doi:10.1109/sst49455.2020.9264050

Multi-Objective Approach for Hydro Governors Control Tuning

2020· article· en· W3108638943 on OpenAlexaff
Harold R. Chamorro, Francisco Gonzalez-Longat, Danijel Topić, Maude Josée Blondin, Vijay K. Sood, Wilmar Martínez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsOntario Tech UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsRenewable energyWind powerHydropowerPhotovoltaic systemElectric power systemAutomotive engineeringPumped-storage hydroelectricityElectricity generationInertiaDistributed generationIntermittent energy sourceEngineeringEnvironmental scienceElectrical engineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.200
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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