Online loss minimization based direct torque and flux control of IPMSM drive
Bibliographic record
Abstract
With the advent of high energy rare earth magnetic material such as, third generation neodymium-iron-boron (NdFeB), permanent magnet synchronous motor (PMSM) is becoming more and more popular in high power industrial applications (e.g., high-speed railway) due to its advantageous features such as high energy density, stable parameters, high power factor, low noise and high efficiency as compared to the conventional ac motors. Over the years, vector control and direct torque and flux control (DTFC) techniques have been used for high performance motor drives. But, the DTFC is faster than that of conventional vector control as the DTFC scheme doesn't need any coordinate transformation, pulse width modulation (PWM) and current regulators. The DTFC utilizes hysteresis band comparators for both flux and torque controls. Most of the past researches on DTFC based motor drives mainly concentrated on the development of the inverter control algorithm with less torque ripple as it is the major drawback of DTFC. The torque reference value is obtained online based on motor speed error between actual and reference values through a speed controller. Traditionally, researchers chose a constant value of air-gap flux reference based on trial and error method which may not be acceptable for high performance drives as the air-gap flux changes with operating conditions and system disturbance. Efficient high performance drives require fast and accurate speed response to cope with disturbances and algorithm to minimize motor losses. \nHowever, if the reference air-gap flux is maintained constant it is not possible to control the motor losses. Therefore, this thesis presents a novel loss minimization based DTFC scheme for interior type PMSM drive so that the drive system can maintain both high efficiency and high dynamic performance. An online model based loss minimization algorithm (LMA) is developed to estimate the air-gap flux so that the motor operates at minimum loss condition while taking the general advantages of DTFC over conventional vector control. The performance the proposed LMA based DTFC for PMSM drive is tested in both simulation and real-time implementation at different operating conditions. The results verify the effectiveness of the proposed flux observer based DTFC scheme for PMSM drive.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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)
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 teacher head, 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".