Experimental Validation of a Mitigation Method of Ferranti Effect in Transmission Line
Why this work is in the frame
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Bibliographic record
Abstract
Electric power transmission networks should be operated in efficient, safe, and reliable conditions. To improve the stability and transfer capability of power transmission, it is necessary to mitigate the Ferranti effect. This paper investigates the impact of increasing the length of the transmission line on its receiving end voltage under no-load conditions. A variable shunt reactor compensation for transmission lines is used to control the voltage level at different lengths of the transmission line. The proposed method demonstrates that the value of the shunt reactor required to maintain the receiving end voltage can be estimated. Moreover, the system is modeled using the PowerWorld simulator, and the effectiveness of the proposed model has been verified by experimental results. The experimental results demonstrate the efficiency of the proposed methodology and match the simulation results, which are then validated by simulating the WSCC 9-bus and IEEE 30-bus test systems.
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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