Dynamic Phasor Modeling and Control of a Single-Phase Single-Stage Grid-Connected PV System
Bibliographic record
Abstract
The increasing deployment of inverter-based sources in power systems and microgrids, and the concomitant reduction in system inertia, have made the study of system dynamic interactions and stability, essential. Detailed inverter models are accurate but computationally inefficient for such studies. This paper presents a dynamic phasor (DP) based model of a single-phase single-stage photovoltaic (PV) inverter. Compared to the existing DP-based PV inverter models in literature, the proposed DP-based PV inverter model includes the PV model and maximum power point tracking (MPPT) dynamics. The DP-based PV inverter model is then linearized around an equilibrium point and the resulting small-signal model is used to develop equations required for obtaining suitable control gains. The DP-based model is built on MATLAB/Simulink and then validated against a detailed model developed in Simulink/Sim-scape. Simulation results confirm the high efficacy of the DP model in predicting dominant harmonics in the AC/DC sides of the inverter, using a simulation step size 50 times larger than the detailed model’s step size. The DP-based PV inverter model is well-suited for system-level study of inverter-based microgrids.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".