Power sharing control strategy for a no-storage hydrokinetic-diesel system in an isolated AC mini-grid
Bibliographic record
Abstract
The operation of diesel engine generator sets (gensets) with light loading leads to deterioration in the engine performance and efficiency. This problem is intensified in no-storage hydrokinetic-diesel systems since the genset is usually oversized to make-up for the potential shortage of renewable energy. In this paper, a new power sharing control strategy has been developed which minimizes this issue using a hydrokinetic energy conversion system (HKECS) that employs a diode AC-DC converter and voltage source inverter. The genset works with a frequency vs. power droop control. The HKECS is controlled with maximum power point tracking unless the grid frequency rises above a limit that indicates the underloading of the genset. At this point, a controlled DC load is activated so as to decrease the power injected by the HKECS thus regulating the grid frequency at this operating point. The proposed strategy is verified with a set-up with rotating machines and power electronic converters controlled via a dSPACE® system. The dynamic and steady-state operation of the system including power balancing, voltage and frequency control are presented.
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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 | 0.001 | 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".