Automatic generation control of a nuclear-renewable hybrid power system using optimal PID controller
Bibliographic record
Abstract
This study investigates the Automatic Generation Control (AGC) of a nuclear-renewable hybrid power system using optimized PID controllers combined with Superconducting Magnetic Energy Storage (SMES). The system is modeled in MATLAB Simulink using state-of-the-art transfer functions, with the pressurized water reactor (PWR) simulated using a point kinetics model to accurately capture dynamic behavior. Six load variation scenarios involving changes of 100–200 MW in the NPP and 5–20 MW in the renewables are analyzed to assess system stability. Optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) are employed to fine-tune the PID gains. The results show improved frequency control: the NPP stays within 49.99–50.14 Hz, an improvement over the previous range of 49.97–50.48 Hz, while the renewables stabilize at 49.99–50.05 Hz, a significant improvement from 46.98–72.30 Hz. These findings confirm improved grid stability and control during sudden load changes.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".