Novel Control of Unified Power Flow Controller Using Modified Invasive Weed Optimization based PI Controllers
Bibliographic record
Abstract
Electrical transmission system especially AC system is commonly established in many countries to transmit electric power from one location to another location efficiently. In order to make AC transmission system efficiently to transmit more power at rated voltage, FACTS devices are commonly employed at various locations. Among many, UPFC device is frequently employed to make better power flow in the transmission system. This UPFC is facilitated between generating unit and distribution system. Two converters are linked with a common dc-link to establish an UPFC device and proper control methodology will be implemented to make an efficient power flow from generation to distribution point. A novel control methodology must be incorporate to achieve an effective operation under various operating conditions. Proposed control methodology utilized various PI controllers and generally conventional PI controllers may suffer from constant gains. Hence, MIWO is developed to update gains of various PI controllers utilized in the proposed control method. Results in terms of convergence are compared among proposed MIWO with existing GWO and ACO methods. MATLAB/Simulink platform is used to simulate the proposed methodology and presented various results under different operating conditions. Comprehensive responses are included in the results section, accompanied by thorough analysis across multiple case studies.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".