Computation-Efficient Solution to Open-Phase Fault Tolerant Control of Dual Three-Phase Interior PMSMs With Maximized Torque and Minimized Ripple
Bibliographic record
Abstract
For dual three-phase interior permanent magnet synchronous machines (DT-IPMSMs), open-phase fault (OPF) can result in significant average torque reduction and harmonics in the output torque and speed, which prevent the machines from a reliable and safe operation. Indeed, these adverse effects are mainly due to significant harmonics in the stator currents caused by OPF. This article investigates fault-tolerant control (FTC) of DT-IPMSM under OPF and proposes a computation-efficient FTC solution to maximize the average torque and minimize the fault-induced torque and speed ripples. In the proposed FTC, the open-phase model is first derived, and optimal stator currents are then derived to achieve maximized average torque and minimized fault-induced torque harmonics. The computation efficiency enables the proposed solution, the capability of FTC, under both the steady-state and transient conditions. Moreover, the proposed FTC can eliminate the harmonic current components in the torque contributing frame and, thus, reduce the harmonic losses, and nonlinear inductance maps are employed to consider magnetic saturation. The proposed FTC is compared with existing methods and evaluated with experiments on a laboratory DT-IPMSM under various operating conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".