LCIs and PWM-VSIs for the Petroleum Industry: A Torque Oriented Evaluation for Torsional Analysis Purposes
Bibliographic record
Abstract
This paper proposes an analytical time and frequency domains evaluation of torque harmonics produced by high-power load-commutated-inverters (LCIs) and pulsewidth-modulated (PWM) voltage-source inverters for adjustable speed applications. These drive systems are used in petrochemical and mining industries, where driven loads have multi-inertia or long shafts that are potentially subject to torsional mode excitation. Such excitation may lead to accelerated fatigue, system failure, and loss of production, and in turbine-generator sets, to system blackout. The investigated power conversion topologies are multi-pulse LCIs (6/6 and 12/12-pulse systems), parallel connected three-level neutral-clamped-converters with non-interleaved and interleaved PWM signals, as well as series-connected H-bridge multi-level inverters, single thread and four threads supplying a 12-phase machine and parallel connection of four threads converter systems with interleaved PWM commands. They are the most used variable frequency drives in the megawatt range, both with synchronous and induction machines, in the petrochemical industry. The time and frequency domains of the machine's air-gap torque is calculated from the motor's voltage and stator's current waveforms, this proven method is independent from the type of ac machine. More than 50 simulated operating points are discussed and experimental results for NPC and cascaded inverters are also presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".