Frequency Control of Micro Grid with wind Perturbations Using Levy walks with Spider Monkey Optimization Algorithm
Bibliographic record
Abstract
Frequency and voltage controls are the two main challenges in the micro grid operation both in the grid connected and autonomous mode due to the presence of uncertain renewable sources. Since economic micro grid operation relies on fluctuating renewable sources such as wind and solar, the task of maintaining frequency within the limits for smooth operation of micro grid demands advanced controller action. Keeping this in mind, a panoptic exploration to search space has been accomplished using proposed eagle strategy for optimizing the gains of PI controller employed in controllable generating units in the islanded micro grid. The proposed eagle strategy which made the search process two fold i.e., coarse search by levy flights and an intensive local search by spider monkey algorithm. The proposed strategy has been tested on typical micro grid test system and also on real world Bella Coola micro grid in British Columbia, Canada. Frequency model of systems were developed in SIMULINK/MATLAB and the simulation results for different scenarios confirms that the proposed strategy performs better and the results are compared with few prominent algorithms to ascertain its superiority in finding better gains of PI controllers.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".