The Effect of PEMFC on Power Grid Using Advanced Equilibrium Optimizer and Particle Swarm Optimisation for Voltage Sag Mitigation
Bibliographic record
Abstract
Due to the integration of various distributed generation resources into power systems, in particular, the integration of proton exchange membrane fuel cell (PEMFC), the electrical power grid (EPG) becomes more comprehensive and has power quality (PQ) issues like voltage/current unbalance, harmonics, voltage sag, etc. The issue of voltage sag arises from sudden changes in the EPG operating conditions. In this paper, the Advanced Equilibrium Optimizer (AEO) and Particle Swarm Optimization (PSO) are proposed as solutions for EPG disturbance in partially voltage sag issues caused by three fault scenarios: single line-to-ground faults (SLGF), double line-to-ground faults (DLGF), and three line-to-ground faults (TLGF). Both of proposed controllers are set up by applying AEO and PSO, and their preponderances are demonstrated by comparison with a conventional PI controller. The simulink model of PEMFC-EPG interconnection system is carried out in MATLAB/Simulink environment. The results of PEMFC-EPG interconnection using AEO and PSO are impressive and persuasive in terms of the voltage sag elimination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".