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Performance and Control Enhancement of Two Area Load Frequency Control Using Tandem Compound Turbine

2019· article· en· W2999360871 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2019 Innovations in Power and Advanced Computing Technologies (i-PACT) · 2019
Typearticle
Languageen
FieldEngineering
TopicFrequency Control in Power Systems
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsAutomatic frequency controlRenewable energyComputer scienceElectric power systemController (irrigation)GridTurbineFrequency gridAutomotive engineeringPower (physics)Control engineeringEngineeringElectrical engineeringVoltageTelecommunications

Abstract

fetched live from OpenAlex

Load frequency control is a virtual challenging problem in a realistic complex power system network. Power demand on the grid is unpredictably varying in nature and consequently grid frequency also varies. To alleviate frequency fluctuations on the grid, the renewable energy resources are interfaced with the grid. Renewable resources are abundant and eco-friendly, but the power generation from these sources is intermittent in nature. To provide major contribution of power generation from the renewable energy resources and to reduce cost of unit power generation in control areas, an effective controller is required. Although some controllers will reduce the errors in the system, they will not enhance the system performance. However, some controllers may affect the system. In order to enhance the system performance traditional turbine is replaced with two stage tandem compound turbine (TDC).By incorporating the TDC, the system performance is greatly improvised and it is shown by the help of Performance Indices. The system is verified by Matlab/Simulink

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.230
Teacher spread0.223 · 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