Fast analytical calculation-based modeling method and fault ride-through parameters selection to enhance short-term voltage stability of grids with large-scale photovoltaic power plants
Bibliographic record
Abstract
• Dynamic interactions between the grid voltage and the PVPP’s FRT behaviors is formulated as a real-time varying impedance. • A simulation-free analytical method is proposed for fast calculation of STV dynamic trajectories of the grid with PVPPs. • The dominant FRT parameters of PVPPs are selected and validated based on the recommended STV evaluation index. Large-scale centralized photovoltaic power plants (PVPPs) connected to a power grid may cause short-term voltage instabilities during fault ride-through (FRT) processes. However, analyzing and optimizing short-term voltage characteristics requires extensive time-consuming simulations due to complex dynamic interactions between the grid voltage and FRT behaviors of PVPPs. To solve this problem, an analytical simulation-free method is proposed in this paper. Firstly, the coupling between the grid voltage and the PVPP’s FRT behaviors is formulated as a real-time varying impedance. Then, a fast analytical method is proposed to derive the grid short-term voltage dynamic trajectories during the complete FRT processes, and the non-monotonic impact of the PVPP’s FRT parameters on short-term voltage stabilities is demonstrated. Further, a selection method for the PVPP’s FRT parameters is proposed and verified using a regional grid. The proposed method improves the grid’s short-term voltage stability with small computational burdens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".