Voltage stability constrained line‐wise optimal power flow
Bibliographic record
Abstract
The purpose of optimal power flow (OPF) is to optimise an objective function subject to a set of operating constraints. It remains an active research area because of using the bus‐wise power balance equations, which leads to a non‐linear solution space, resulting in OPF yielding local optimal solutions, thereby causing significant economic loss. In this study, first, a new line‐wise OPF (LWOPF) formulation is proposed. Thereafter, a maximum loadability factor, as a voltage collapse indicator, is derived and combined with LWOPF constraints to form a voltage stability constrained LWOPF (VSCLWOPF) model. As the line‐wise power balance equations are based upon the square of voltage magnitudes, it results in significant improvement in the solution space and lower‐order terms in all computational steps. The LWOPF and VSCLWOPF formulations, are solved using non‐linear optimisation technique, tested on several benchmark and real power systems. Results show that the proposed LWOPF is accurate, provides monotonic convergence, and scales well for large systems. It provides a better solution and is consistently faster, up to twice the speed of MATPOWER, due to reduced computational needs. Results of VSCLWOPF show that, for the same voltage stability level, the solution costs less than that obtained by classical bus‐wise OPF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".