Mitigation of Power Quality Problems Using Fuzzy Logic-Based Unified Power Quality Conditioner (UPQC)
Bibliographic record
Abstract
This suggested research work provided a distributed generator-based unified power quality conditioner with a decreased rating and a star-connected transformer as a means of enhancing power quality. The unified power quality conditioner (UPQC), a Y-connected transformer, and an LC filter are all included in this work. When the source voltage is out of balance, the hybrids of the approach greatly enhance UPQC performance. The quality conditioner that is suggested here is intended to correct issues with the voltage and current quality of delicate loads and to reduce load current harmonics when the supply is distorted. In this case, the DC link control method has been implemented using a fuzzy logic-based controller. A 500 kVA grid model is taken into consideration, along with an analysis and description of the suggested solution. The results were produced using the power system block set toolboxes in the MATLAB/Simulink environment. According to the thorough simulation results, hybrid UPQC with distributed generation has a greater ability to reduce the voltage sag effects and swell and to suppress the load current harmonics, phase current harmonics, and neutral current when the supply is distorted. To validate the results generated by the proposed method, the hybrid methodology yields better results when compared to the traditional UPQC method.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".