Automatic generation control of a nuclear-renewable hybrid power system using optimal PID controller
Why this work is in the frame
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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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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) | 0.001 | 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 it