An Optimization‐Based Line‐Wise Approach for Accurate Radial Distribution System State Estimation
Bibliographic record
Abstract
ABSTRACT Distribution system state estimation (DSSE) is an essential tool for the effective operation and management of modern distribution systems. A common challenge in DSSE is ensuring accurate estimates despite limited real‐time measurements and high pseudo‐measurement errors. This paper presents a novel line‐wise state estimator (LW‐SE) for radial distribution systems, leveraging conic quadratic optimization to transform the non‐convex state estimation problem into a convex one. This transformation enhances the accuracy of the state estimation process. Unlike traditional methods, the LW‐SE formulation uses line impedances rather than admittances, addressing issues associated with low‐impedance branches and leading to more stable power flow representations. Furthermore, the method accommodates diverse types of measurements without requiring paired active and reactive power measurements, their equivalent forms, or phase angle measurements as inputs—while still enabling accurate phase angle estimation. Results of case studies and comparisons with traditional state estimators (T‐SE) demonstrate the effectiveness of the LW‐SE with accuracy improvement ranging from 60% to 82% in scenarios with low availability of real‐time measurements and high errors in pseudo‐measurement. In scenarios involving gross measurement errors, the LW‐SE consistently delivered lower MAPEs than the weighted least squares (WLS) and weighted least absolute value (WLAV) state estimators, while maintaining computational efficiency. These findings underscore the LW‐SE's suitability for modern distribution system applications.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".