Comparative Analysis of the Utilization of Supercapacitor Versus Grid-Tie Inverter Regenerative Braking Methods for Elevator Systems
Bibliographic record
Abstract
In this paper, supercapacitors and grid-tie inverters are compared as means of integrating regenerative braking functionality into elevator systems. In dynamic braking, a conventional motor drive is unable to utilize energy during braking periods because it is powered by a rectifier in which energy flows in only one direction. Typically, braking resistors are used to dissipate any excess energy generated that can result in a breakdown of the device if left uncontrolled. Alternatively, supercapacitors or grid-tie inverters can be installed to the DC-link and the energy either stored for future use or fed to the power grid, thus preventing energy waste. Models for these two regenerative braking methods are constructed and simulated in the Simulink environment in order to compare their energy efficiencies under several unique elevator usage patterns. In usage patterns that allow the motor to generate energy within a long duration, the simulation results show the supercapacitor option is less of an improvement in energy efficiency than the grid-tie method due to energy flowing through two energy conversion devices. On the contrary, in usage patterns that limit regeneration within short bursts, the grid-tie inverter option is less efficient than the supercapacitor method due to output current limitations.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".