Design of Cloud Computing-Based Control Algorithm for Hybrid Power System in Smart Grid Applications
Bibliographic record
Abstract
Hybrid renewable energy (HRE) models are those that have two or more renewable sources connected together with some conventional sources to serve the demand load. The objective of this article is to present a cloud-based HRE model in which a Legendre wavelet embedded neurofuzzy (NF) indirect adaptive (LNFIA) maximum power point tracking (MPPT) control of photovoltaic (PV) system is implemented for the extraction of maximum power and a Hermite wavelet-based NF indirect adaptive control (HNFIA) of solid oxide fuel cells (SOFCs) for obtaining a swift response in a grid-connected HRE system. The implementation of these two smart controls for PV systems and SOFC maintains the tradeoff among power generation and load demands. The proposed HRE model when connected with cloud can be implemented for large-scale applications. An extensive experimental analysis is carried out to ensure the effectiveness of the proposed model. The result analysis verified that the proposed model shows an effective 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".