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Record W1556536264

Développement des modèles d'essais et application à l'identification des machines synchrones et asynchrones triphasées

2007· dissertation· fr· W1556536264 on OpenAlexaboutno aff
C. Jolette

Bibliographic record

VenueDepositum (Université du Québec en Abitibi-Témiscamingue) · 2007
Typedissertation
Languagefr
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhysicsArt
DOInot available

Abstract

fetched live from OpenAlex

Le présent mémoire développe des modèles d'essais de la machine asynchrone et synchrone dans le but d'en faire l'identification des paramètres. Pour la machine asynchrone, les modèles en mode moteur et en mode générateur sont examinés. Ils permettent de prédire les essais de démarrage très appropriés pour l'estimation des paramètres et l'essai d'amorçage en génératrice asynchrone auto excitée connectée à une charge locale. Pour la machine synchrone, des formulations analytiques plus exactes des tensions d'armatures et du courant de charge sont développées à partir du modèle hybride. Ces expressions permettent de mieux prédire des essais de décroissance de flux statorique tels que: le délestage, le court-circuit de champ et l'enclenchement de charge. Afin de tenir compte de l'évolution des grands courants de charge, un modèle hybride incluant la charge est proposé. Tous les essais sont mis en oeuvre à l'aide de « Matlab » et validés avec des essais expérimentaux obtenus sur des petites machines du laboratoire au centre de formation de Hydro-Québec.
\nUne procédure d'identification associant l'estimateur aux moindres carrés asymptotiques et un algorithme d'optimisation de type Quasi Newton est utilisée pour la détermination des paramètres des machines synchrones et asynchrones à partir des essais développés. Deux types de validation permettent de mieux apprécier la qualité des résultats obtenus: la validation directe (avec le même essai) et la validation croisée (avec un essai différent de
\ncelui utilisé pour l'identification). Enfin, la technique de mise en oeuvre numérique des essais et la procédure d'identification sont organisées sous forme de logiciel interactif pouvant faciliter la simulation et l'identification des machines asynchrones et synchrones à partir des essais proposés.
\n
\nThe present work develops test models for parameters identification of induction and synchronous machines.
\nFor the induction machines, models in the generator and motor modes are studied. In the motor mode, the proposed induction machine model is very appropriate to perform the
\nstarting test which is a very interesting test for the parameter estimation. Parameters obtained from the motor mode are after, used for the cross-validation in the generator mode. For the synchronous machine, exact analytical formulations of armature voltages and the
\nfield current following a stator development tests are derived. Load rejection tests, line switching tests and field short-circuit tests are performed from these analytical formulas.
\nSpace state hybrid model including the connected load model is also proposed for high armature current loads.
\nAU models are implemented using the « Matlab » software. Experimental data obtained from Hydro-Quebec laboratory are used for the model validation. An identification process including the asymptotic least squares estimator and the Quasi Newton type optimization algorithm is applied for the machine parameter determination using the above mentionned tests. A direct validation and a cross-validation are used to assess the robustness of proposed models and the identification process. Finally, the numerical method for the test prediction and the identification procedure are
\norganized in a tutorial software form for simulations and parameter estimation experiments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.229
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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
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
Published2007
Admission routes1
Has abstractyes

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