A Comparative Study of Ground Fault Analysis for a Practical Case of a Transmission Line Equipped with Different Series FACTS Devices
Bibliographic record
Abstract
Due to the rising power demand and increasing population worldwide, electrical power networks have been extensively growing and striving to satisfy the escalating loads. This necessitates the need for using Flexible AC Transmission System (FACTS) devices which have become indispensable during normal and abnormal operating conditions. This paper investigates the impact of using series FACTS devices, namely Thyristor Controlled Series Capacitor (TCSC), GTO Controlled Series Capacitor (GCSC) and Thyristor Controlled Series Reactor (TCSR), on the impedance and power flow of a practical 400 kV transmission line in the Algerian power network. It also investigates the effect of varying the fault resistance on short-circuit calculations in the case of a phase to ground fault that occurs at the end of the compensated line. Analytical formulas of the employed FACTS devices, the system model under fault and short-circuit calculations are deduced and presented in the paper. Simulations results obtained using MATLAB are demonstrated for the compensated line and without compensation. These simulations are compared to show the effect of using these devices for the studied cases.It is concluded that GCSC provides better performance in the active and reactive power flow of the line under normal operating conditions and in reducing the fault current during abnormal operating conditions when the fault resistance increases. On the other hand, TCSR shows a better performance in maintaining higher voltages under fault with the increase of fault resistance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".