Contribution of PV generators with energy storage to grid frequency and voltage regulation via nonlinear control techniques
Bibliographic record
Abstract
This paper proposes a nonlinear control strategy for a hybrid PV-battery system insuring frequency and voltage support of the power system. The hybrid system includes a PV panel and battery connected to three-phase DC-AC inverter via DC-DC boost converter and bidirectional DC-DC boost converter. A synchronous generator represents the power grid. The voltage regulators control DC-DC boost converter and DC-AC inverter while the frequency regulator controls the bidirectional DC-DC boost converter. A conventional MPPT is used to adjust the reference for nonlinear PV voltage regulator. The voltage regulator is designed based on multi-input multi-output exact feedback linearization technique. It consists of a module that uses the terminal voltage deviation to generate q-axis voltage component. A module that maintains the DC-link voltage is also added to generate d-axis voltage component as well. The proposed frequency regulator includes a module that changes the reference signal of a battery current control module when the frequency deviation is significant. The battery current regulator is designed based on partial input-output feedback linearization strategy. The proposed control system is evaluated in simulation. The results reveal that with the proposed control scheme, the PV-battery generator reacts like a conventional synchronous generator when the grid frequency changes considerably.
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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 | 0.000 | 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".