Performance evaluation of electrostatic precipitator transformer by considering power quality
Bibliographic record
Abstract
• The distribution of the electromagnetic force on the winding of the set is analyzed. • Simulating the electric field distribution in the electrostatic precipitator. • The power quality of the electrostatic precipitator is improved by a passive filter. • The total harmonic distortion of the transformer/ rectifier set is reduced. • Enhancing the power factor of the transformer/ rectifier set by the passive filter. Electrostatic precipitators (ESPs) are industrial emission control units. The ESPs are designed to trap and remove dust particles from the exhaust gas stream by using the force of an electric field, which is produced by a high-voltage power supply. One of the main problems of these power supplies is the presence of non-linear elements that adversely affect the power quality of the system. In this paper, a transformer/rectifier (T/R) set is modeled as a high-voltage power supply for an ESP, and the distribution of electromagnetic forces on the set is analyzed using the Finite Element Method (FEM). Additionally, the current waveform and Total Harmonic Distortion (THD) of the T/R set are modified by designing and utilizing a passive filter for the system. First, the working principle and operation of the T/R set are modeled. Then, the electromagnetic design of the T/R set is evaluated by the finite element method and the transient analysis of the flux densities, and associated radial and axial components of electromagnetic forces distribution on the T/R set. Afterward, a wire-plate ESP configuration is modeled and analyzed based on the Electric potential and Electric field. Finally, the design method of the passive filter is explained, and it is shown that the utilization of the designed passive filter has significantly reduced the THD of the T/R set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".