Performance Comparison of FACTS (UPFC) and HVDC in Power Flow Optimization via Genetic Algorithms
Bibliographic record
Abstract
Power transmission networks play a critical role in linking generation and distribution systems.One key aspect of the network's performance is voltage optimization.This study focuses on comparing the impacts of High-Voltage Direct Current (HVDC) transmission and Flexible Alternating Current Transmission Systems (FACTS), specifically the Unified Power Flow Controller (UPFC), on system voltage stability, grid power losses, and transmission capacity under load fault conditions.This present study develops the IEEE 30-bus and IEEE 57-bus systems as test cases, incorporating Genetic Algorithms (GA) to analyze the effects of HVDC and UPFC integration.The Power System Simulator for Engineering (PSS/E) version 33 software program is used to model multi-terminal UPFC and HVDC.A comparative study is performed between the system's performance with and without HVDC and UPFC under various load conditions in the transmission network.Three load conditions were analyzed.The results demonstrate that for the IEEE 30-bus system, the total active power loss under normal load conditions is reduced by 69.594% after adding UPFC between buses (3-4) and by 75% after introducing multi-terminal VSC-HVDC between buses (2-6) and (2-4).Similarly, reactive power losses are reduced by 74% with UPFC and 73% with multi-terminal VSC-HVDC under the same conditions.For the IEEE 57-bus system, the addition of UPFC and VSC-HVDC improves active and reactive power losses by 49% and 55%, respectively, under normal load conditions.The studied results confirm that connecting HVDC to the system achieves better results in terms of bus voltage profile, a significant reduction in total network power losses, and a higher effective power transfer rate compared to UPFC.Moreover, multi-terminal HVDC transmission delivers greater voltage improvements and larger reductions in total power losses compared to adding UPFC to the same system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".