Variable Frequency Drives-Induced Torsional Stresses in Pumped Hydropower Storage Applications
Bibliographic record
Abstract
Despite consistent maintenance and monitoring equipment installed in pumped storage hydropower (PSH) facilities, many shafts and electrical component failures are reported, possibly resulting from undetected sources. These sources include undetectable vibrations or, in certain conditions, high-frequency mechanical or electrical harmonics. They may induce premature material fatigue and aging, leading to accelerated wear and premature system failure. This paper presents a direct method for plotting Campbell diagrams of large motors supplied by variable frequency drives for torsional analysis purposes. The method is applied to two-level, three-level neutral-point clamped, and seven-level cascaded H-bridge multilevel inverters, commonly used industrially available voltage source inverter (VSI) topologies for pumped PSH plants. These diagrams display the locations where torsional stress components induced by VFDs can interfere with shaft resonance modes. These locations are where threatening torsional stresses can build up. The method simplifies the determination of the magnitude of stimulus forces in the motor airgap that may threaten the shaft. An analytical assessment of the cascaded H-bridge VFD-induced torsional stresses under unbalanced operation is also proposed. The accuracy of the theoretical developments is supported by selected simulations results at different operating points and different fault conditions. Hybrid experimental-numerical VFD-induced harmonic stress analysis is also performed to demonstrate the relevance of the proposed study. Results are crucial for a robust design of the system integration to avoid catastrophic failures.
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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) | 0.000 | 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) | 0.001 | 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".