Stochastic Small-Signal Stability Analysis of Grid-Connected Photovoltaic Systems
Bibliographic record
Abstract
As the penetration level of photovoltaic (PV) generators into the grid is rapidly increasing, the effect of a variable PV power output on the stability of power systems cannot be ignored. Due to the stochastic characteristics of PV power generation, deterministic analysis approaches are not able to fully reveal the impact of high-level PV integration. This paper investigates the impact of the stochastic PV generation on the dynamic stability of grid-connected PV systems by using a probabilistic small-signal analysis approach. The sensitivity of the critical eigenvalue to the variation of solar irradiance is obtained. With the knowledge of the sensitivity relationship and the statistics of solar irradiance data, the probability density function (pdf) of the real part of the critical eigenvalue is approximated by Gram-Charlier expansion. This pdf is then used to calculate the probability of the stochastic small-signal stability of a power system. The impacts of important system parameters on the stochastic stability of the system are also analyzed. It has been found that these system parameters can significantly affect the stochastic stability of the system. Results of Monte Carlo and time-domain simulations of the grid-connected system verify the effectiveness of the proposed stochastic stability analysis method.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".