A study of the impacts of power fluctuations generated from large PV systems
Bibliographic record
Abstract
Installing large grid-connected photovoltaic systems is a new trend in many countries, especially the developed countries. These systems have a number of positive and negative technical impacts on the operation of the electric network. Thus, careful studies should be carried out prior to installing these systems. One of the main characteristics of PV systems that has to be considered when studying their impacts is the fluctuations in their output power. These fluctuations require careful analysis so as to predict their effect on different electric quantities of the feeder under study. The use of typical patterns of PV power in power flow analysis is one way to analyze the impacts of fluctuations. However, so far, there is no specific criterion on how to choose these patterns. In this paper, we propose a criterion that can be used to choose the typical patterns for each season. The number of patterns can be specified by the user based on the inspection of the patterns. The usefulness of the proposed criterion is illustrated through a case study, where the impacts of fluctuations on different quantities of the feeder under study are presented and discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".