Hybrid PSO-HHO Optimal Control for Power Quality Improvement in Autonomous Microgrids
Bibliographic record
Abstract
The focus on resilience, energy security, and renewable energy is increased.Hence the necessity of autonomous microgrids has become more prevalent.These autonomous microgrids can operate independently and provide stable energy to the users.The design and performance of an autonomous microgrid depends on the control system which will play a significant role in its ability to provide reliable and resilient energy.Hence to account this a hybrid PSO-HHO based optimal control strategy is proposed for power quality improvement.A test case of 3.5 kW PV based autonomous micro grid system is considered and implemented in MATLAB/Simulink.The proposed hybrid PSO-HHO based optimal control strategy is compared with PSO and HHO based optimal control strategies.The performance parameters such as PV maximum voltage PVvmax, PV maximum current PVimax, Voltage RMS VRMS, Current RMS IRMS, PV output power PVop, Autonomous grid power Agp, THD, Efficiency, Inverter Losses Invloss are evaluated in all the cases.The proposed hybrid PSO-HHO based optimal control strategy exhibited the mark improved performance.
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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 | 0.001 | 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".