A probabilistic approach for optimal capacitor planning in distribution systems with wind generators
Bibliographic record
Abstract
This thesis proposes a probabilistic approach based on the Cumulant method for optimal capacitor planning in distribution systems with high penetration of wind generators. To account for the problem uncertainties, the probabilistic behaviour of load forecasts and wind generators are modeled using Probability Density Functions. Once the probabilistic framework is defined, an optimization problem can be formulated to minimize the total costs of the capacitors and of the annual energy losses. The optimization problem is then solved by using the Logarithm Barrier Interior Point Method, which provides a linear relationship between the cumulants of load and wind variables and the cumulants of the system parameters and solution cost. The Cumulant method offers a generous advantage in speed, while maintaining acceptable accuracy, as compared to the traditional Monte Carlo Simulation method. The proposed method is tested on a 7-bus and on a 33-bus systems, and the results are reported 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".