Optimization of automatic generation controllers in renewable multi-area power systems using the Fata Morgana algorithm
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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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".