Enhanced Nature-Inspired Meta-Heuristic Algorithm for Microgrid Performance Improvement
Bibliographic record
Abstract
In this article, a new selection technique based on Enhanced Nature-Inspired Meta-Heuristic (ENIMH) optimization algorithm is presented to improve the Microgrid (MG) dynamic performance. Interconnected microgrids have the ability to provide a clean and sustainable energy during normal and emergency operating conditions. The concerned microgrid includes hybrid renewable energy sources (RES) and energy storages systems (ESS). MG achieves a reduced dependency on the electric grid and provides flexible and adaptive energy supply. This paper develops a new selection technique based on ENIMH optimization that distinguishes the degrees of resemblance between the best individual and other individuals of current population. This technique proposes a binary coding of individuals, and is compared to conventional techniques; it allows each individual to occupy a section of the modified roulette wheel selection for the calculated degree of resemblance. This enhanced optimization technique tunes the dynamic PID parameters of microgrid closed loop system. The designed strategy is dependably to locate the arrangement of enhanced parameters to minimize the system frequency fluctuations in the microgrid and to provide the improved dynamic performance by being sensitive to variations for closed loop response under various power and load conditions. The proposed technique has been demonstrated using Matlab/Simulink simulation on the underlined microgrid, where the achieved results confirm the effectiveness of proposed selection method for the reproduction of best individuals to show the improved performance. The proposed technique achieved satisfactory performance for PID-controllers, and provided a good closed loop performance, minimum overshoot and minimum fitness index, in comparison with other well-established methods. The results emphasize that ENIMH optimization algorithm has the exploration and exploitation capability of population best individuals to accomplish the best solutions.
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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 | 0.001 |
| 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".