CONTROL AND DESIGN ASPECTS OF POWER ELECTRONICS CONVERTERS USING PSPICE
Bibliographic record
Abstract
In order to understand the functionality and design aspects of power converters, circuit simulation software PSpice has become an industry standard. In a Power Electronics course the students are required to understand the operating principles of a variety of static power converters using different control techniques to achieve the desired input-output characteristics.This paper presents PSpice-based design projects that can be used as pre-Lab exercises in a Laboratory course accompanying a lecture course in Power Electronics. The students can be made to implement their design in the laboratory with actual hardware components. The transition from design to simulation and finally to experimental verification will aid to strengthen their understanding of the operation, control, and design aspects of power converters. The design projects are geared towards bringing out the importance of power quality and cost issues that are relevant to state of the art circuit design.The design examples in the paper will start with a set of design guidelines and input-output requirements of a given power converter system. The design will involve selection of the proper control algorithm, switching frequency, and input-output filter values to meet the design goals. The following are the basic converter systems that will be covered in the various design projects: single phase and three phase rectifier, single phase and three phase inverters, buck and boost converter.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".