Performance optimisation for novel green plug‐energy economizer in micro‐grids based on recent heuristic algorithm
Bibliographic record
Abstract
This study presents technical issues to enhance the micro‐grids (MGs) performance. Novel distributed flexible AC transmission system (D‐FACTS) is applied on AC/DC MGs to achieve full utilization of distributed generations (DGs), and to improve power quality and system stability. That D‐FACTS is called green plug‐energy economizer (GP‐EE) device, which is proposed with two schemes to be applied on both DC/AC terminals of MG. DGs are used within MGs so that they can meet dynamic load patterns. Proposed design of MG includes photovoltaic, fuel cell, battery, storage system, micro‐gas turbine and wind turbine. GP‐EE device will be controlled by PID modified weighted controller. Dynamic tri‐loop error driven will be enhanced by extra supplementary regulation loops to ensure power factor correction, stabilize buses voltage, reduce feeder losses and power quality enhancement. A recent heuristic optimization technique is called backtracking search algorithm, and is proposed for optimal selection of the scheme parameters of pulse‐width modulation pulsing stage of GP‐EE to dynamically online gains adjustment of PID tri‐loop regulation process and minimization of the global control error. Digital simulations have been validated GP‐EE schemes effectiveness based on without and with modules performance. Achieved results show applicability and improved performance using the proposed technique.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".