On the Performance of the Frame-Angle Controller for <inline-formula> <tex-math notation="LaTeX">$3\phi$</tex-math> </inline-formula> Interconnected PV Systems
Bibliographic record
Abstract
This paper presents the development and performance of a controller for photovoltaic (PV) systems that are gridconnected through a 3φ wavelet modulated (WM) dc-ac power electronic converter (PEC). The proposed controller is designed using decoupled current control loops, which have the d - q-axis components of the currents injected to the grid, as their inputs. The outputs of this controller are command values for the d - qaxis components of the voltages produced by a 3φ WM dc-ac PEC (vId* and vIq*). The values of vId* and vIq* are used to determine the required angleϑ, which aligns the frame spanned by the actual d - q-axis components of the voltages produced by the controlled 3φ WM dc-ac PEC (vIdand vIq). The adjustments of ϑ allow changing the phase angles of sinusoidal reference signals used to generate the wavelet modulation switching pulses. The proposed controller is implemented for performance testing using a 15 kW ac module PV system that is interconnected through a 3φ, 6-pulse, WM dc-ac PEC. The performance of the frame-angle controller is tested for different changes in the power delivered to the grid and system parameters. Test results demonstrate stable, fast, and accurate control actions that are complimented by negligible sensitivity to levels of power delivery to the grid, as well as variations in system parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".