Optimal integral minus proportional derivative controller design by evolutionary algorithm for thermal‐renewable energy‐hybrid power systems
Bibliographic record
Abstract
The goal of this work is to investigate the application of integral minus proportional derivative (IPD) controller for the automatic generation control (AGC) problem comprising of a two‐area thermal system integrated with renewable energy (RE)‐based sources such as wind, solar and fuel cells. In order to facilitate a realistic environment, each thermal system is equipped with a governor dead band, reheat turbine and generation rate constraint. Moreover, each RE‐based power system is modelled by incorporating certain drift and random variations as the key characteristic of RE‐based sources. The control performance of IPD is compared with the PID and PI controllers all tuned using an evolutionary technique genetic algorithm by incorporating a step load perturbation in both areas. In order to verify the effectiveness of the control scheme, detailed performance investigations are carried out using random variations in load perturbations and in RE‐based power. In addition, sensitivity analysis is also included for wider variations in system parameters in order to test robustness. Based on the extensive simulations for robustness and accuracy, it was observed that IPD outperforms the other controllers and therefore serves as a promising solution to the problem of AGC.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".