Algorithms for In-situ Efficiency Determination of Induction Machines
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".