Reliability Based Analysis for Optimum Allocation of DG
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
DGs are gaining more importance and they are expected to drastically change the whole distribution system practices. Distribution network will be no longer a passive termination of transmission network and the concept of active network has been recently introduced to indicate a new kind of distribution with DGs actively involved in system management and operation. One of the most important benefits of DGs is reliability improvement. Therefore, in this paper a new methodology, based on system segmentation concept is proposed in order to select the optimum size and location of DGs required to maximize the system reliability. Moreover, a software was developed with the capability of scanning the distribution system in order to divide it into several operational segments based on the utilized protection devices. The reliability of each individual segment as well as the system reliability was evaluated based on selective reliability indices. Nevertheless, more indices can be taken into consideration for the evaluation process. This concept is applied to a practical distribution system, and the results showed that there is a great relation between size and location of DG and reliability improvement.
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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 | 0.001 | 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 it