Maximum Efficiency Control of PMSM Drives Considering System Losses Using Gradient Descent Algorithm Based on DC Power Measurement
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Bibliographic record
Abstract
This paper proposes a novel method of efficiency improvement in a vector controlled permanent magnet synchronous motors (PMSM) through system level maximum efficiency point determination using current angle as a control variable. Loss models for the inverter and the motor fundamental and harmonic losses, which are capable of being solved online using available terminal measurements in the system are initially developed. The loss models and dc-link power measurement are then used to seek the maximum efficiency angle for different operating conditions using a gradient descent optimization algorithm. The developed method is robust against changes in inductances due to saturation and cross saturation with loading conditions as well as temperature effects. The effectiveness of the developed method in improving the system efficiency is verified and compared with conventional maximum torque per ampere method. The proposed strategy has been validated on a laboratory interior PMSM, and the efficiency has been calculated for different speed and torque conditions. The experimental validations confirm the effectiveness of the proposed solution in improving the motor drive system energy efficiency.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it