Exploring The Effect Of Different Load Models On System Reconfiguration
Why this work is in the frame
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Bibliographic record
Abstract
The self-healing framework has always aimed to restore as many loads as possible following a fault, which is usually accomplished by reconfiguring the power network. In most cases, the load is assumed to be a constant power load, and thus has no effect on the final network topology. According to recent research, the type of load and its equivalent model could influence how the network is reconfigured. This paper highlights and investigates the impact of various load types on the reconfiguration/self-healing scheme. A sensitivity analysis was performed on an islanded IEEE - 69 bus system with probabilistic wind turbines (WTs) and several dispatchable distributed generation (DGs) units. The cases examine the system with separate load types and then with a mix of load types. The results show that the load type affects the final topology of the system as well as the amount of energy not served after reconfiguration.
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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 | 0.000 | 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 it