Assessing the Impact of Harmonics and Interharmonics of Top and Mudpump Variable Frequency Drives in Drilling Rigs
Bibliographic record
Abstract
Declining extraction costs and process efficiencies have incented the revival of the oil and gas sector postrecession. The application of drilling rigs has been done more efficiently. These loads are very large, temporary in nature, and mobile. The drilling of each well can be done as quickly as a few hours and typically not more than a few days. These massive loads are complex to own, maintain, and operate, as a result few specialized drillers share the market. Because of the requirements for variable speed and control, drilling rigs are driven by variable frequency drives (VFDs), which are in their clear majority of older technologies such as six-pulse thyristor bridge or SCR-driven motors. These technologies are known to impose steep ramp rates (up and down) as well as rich harmonic content and variable, typically low, power factor. Furthermore, the locations of drilling rigs are typically remote, where distribution systems, if present, are weak, which lead to large background voltage distortions when such loads are present. This paper presents measurements, simulations, and analytical assessments of the characteristics and effects of harmonics and interharmonics (IHs) generated by top-drive and mud-pump VFDs in a large drilling rig operated in Alberta, Canada. Measurement results reveal very high harmonic and IH generation. To further refine the impact assessment of these harmonic emissions, the harmonic attenuation effect of the VFDs is quantified to avoid overestimating the harmonic generation of the loads. The thermal impact of the current harmonics on the coupling transformer is also quantified.
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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 | 0.000 | 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)
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".