Optimization of automatic generation controllers in renewable multi-area power systems using the Fata Morgana algorithm
Why this work is in the frame
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Bibliographic record
Abstract
The increasing integration of renewable energy sources introduces severe intermittency in multi-area power systems (MAPS), resulting in significant voltage and frequency fluctuations. This study addresses this problem by implementing an automatic generation control (AGC) framework for a two-area hybrid power system composed of solar, wind, and thermal units. Four types of controllers (PI, PIDn, fractional-order PI (FOPI), and predictive PIDn (PPIDn)) were optimized using four recent metaheuristic algorithms: golden jackal optimization (GJO), educational competition optimizer (ECO), escape algorithm (ESC), and the newly proposed Fata Morgana Algorithm (FATA). The results demonstrate that the FATA-optimized PIDn controller provides the best dynamic performance, achieving an ITAE value of 0.18676, which represents an improvement of over 4.6% compared to the best established optimizer (ESC). Real-time validation on the OPAL-RT OP5707 platform confirmed the practical feasibility of the proposed FATA-based control strategy, verifying its ability to enhance frequency stability. These findings highlight the novelty and efficiency of FATA in optimizing AGC parameters for renewable-based multi-area power systems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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