An Optimized Frequency Control of Green Energy Integrated Microgrid Power System using Modified SSO Algorithm
Bibliographic record
Abstract
Abstract This paper proposes a modified sperm swarm optimization (MSSO) technique for automatic load frequency control (ALFC) of bio and renewable energy (RE) integrated Microgird (MG) system. A chaotic search based on a one-dimensional (1D) chaotic map is adopted to intensify the exploitation and exploration characteristics of sperm swarm optimization algorithm. The proposed MSSO technique is used to tune the gains of proportional integral derivative controller to regulate the frequency of MG system through minimization of integral time absolute error of frequency deviation. The effectiveness of the technique is evaluated in terms of steady state and transient performance indices for the response of frequency and power deviation. In addition, to validate the robustness, a sensitivity analysis is carried out under varying load condition, change in system parameter, and real-time variation in RE sources. The results obtained for the aforementioned cases show that the proposed MSSO tuned technique outperforms other techniques (Salp swarm algorithm, Particle swarm optimization, and sperm swarm optimization) in terms of steady state and transient indices. The real-time implementation of proposed controller for MG system is validated in Hardware-in-loop analysis with its stability analysis in frequency domain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".