Algorithms for In-situ Efficiency Determination of Induction Machines
Notice bibliographique
Résumé
Robust structure, high reliability and low maintenance costs allow induction motors to be widely-used in various industrial applications. In recent decades, due to the increased concerns on global warming, and the effort to enhance the efficiency of tools, equipment, and systems, efficiency of induction machines (IMs) has received a lot of attention. The rated efficiency of an IM can be found on the nameplate. However, it is affected by aging, ambient temperature, load, supplied voltage and other technical reasons. Furthermore, based on NEMA MG 1 standard, the actual efficiency of an IM may vary from the nameplate value. As a result, efficiency estimation of IMs is essential to evaluate the efficiency of the whole system and energy cost. \nApplying available international standards to in-situ machines needs load decoupling and in some cases, the no-load/locked-rotor test is required. This is not allowed with in-situ machines. Therefore, having a non-intrusive method which is capable of estimating the efficiency of the machine by using only available data such as the input voltages, currents, active power and nameplate data is necessary. \nThis thesis investigates in-situ methods to determine the efficiency of IMs and three related subjects are addressed. First, an optimization based algorithm is proposed to determine the efficiency of the IM at different loads. This algorithm is proven to have minimum intrusiveness and only uses the data of one operating point of the machine. Assumptions and techniques to increase the accuracy of the algorithm are addressed. The proposed algorithm is then applied to two conditions. In the first condition, the required input data are recorded when the machine reaches its thermal stability and final temperature rise of the machine is used as an input. In the second condition, the required input data are recorded 30 minutes after start of the machine and then the machine final temperature rise is predicted. Two approaches are proposed to predict final temperature rise and are based on machine insulation class and temperature rise of the machine in the first 30 minutes of operation of the machine after start. \nMoreover, a method is proposed to determine the range of IM equivalent circuit parameters and improve the probability of converging to the correct answer. The method is based on the nameplate data of the machine and empirical results provided by Hydro-Québec. The method is also improved by using the operating data of the machine. The proposed range determination is very helpful for in-situ applications where the output power of the machine is not available. \nSecond, two dimensional finite element analysis (FEA) is used to predict the efficiency of the IMs at different loads. Two methodologies are adopted. In the first methodology, the losses are calculated directly using FEA while in the second one, the equivalent circuit parameters are first estimated using FEA and then the efficiency at different loads are estimated using the equivalent circuit parameters. To improve the results, a simple formula based on the rated power of the machine is proposed to evaluate the friction and windage losses also known as the mechanical loss of the machine. The proposed formula is applicable for 4-pole 60 Hz IMs and was achieved after study of more than 100 IMs of this type. \nThird, the effects of the adjustable-speed drives on the losses and efficiency of the IMs are addressed. The direct torque control and scalar control schemes implemented by an industrial drive are employed to control two types of IMs. Two IMs designed for direct-fed application and two other IMs designed for PWM applications are studied while method B of IEEE Std-112 is applied to segregate the losses. Variations of different losses at drive-fed and direct-fed conditions are compared and results are discussed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».