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Predictive Based Control Algorithm for Hybrid Diesel-Battery Standalone Power Generation System

2019· article· en· W3017075636 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 8th International Conference on Power Systems (ICPS) · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsCegep de Sept IlesÉcole de Technologie Supérieure
Fundersnot available
KeywordsDiesel generatorComputer scienceBattery (electricity)InverterControl theory (sociology)Power (physics)MATLABModel predictive controlThree-phaseVoltageDiesel fuelEngineeringAlgorithmAutomotive engineeringControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

In this paper, control of hybrid diesel-battery standalone power generation system is investigated. Power management and power quality improvement at the point of common coupling (PCC) are achieved by controlling the three-phase inverter which is supported by a battery energy storage system (BESS) using predictive based control algorithm. The predicted output filter voltage and current based on the behavior of the inverter switches are used as inputs for the developed control algorithm while the diesel generator (DG) is connected or not. The developed control algorithms are validated with Matlab/Simulink under severe conditions such as sudden connection and disconnection of the DG in presence of non-linear load.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.020
GPT teacher head0.261
Teacher spread0.241 · 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