PMU placement to reduce state estimation bias considering parameters uncertainty
Bibliographic record
Abstract
In this paper, a novel analysis is performed to determine the bias of Weighted Least Squares (WLS) power system state estimator to find out how much different placements of Phasor Measurement Unit (PMU) will affect the state estimator's bias behavior whereas the network parameters are subjected to different quantities of uncertainties for a given measurement uncertainty. The lack of unbiasedness is an important defect of state estimators hence, for each network parameters uncertainty the bias of state estimator is computed. To determine how much the state estimator is biased, using the classical hypothesis test approach a threshold is assigned. The analysis is simulated on IEEE 14-Bus power network test case. To have an idea about the visualization of bias test concept, the bias of voltage magnitude and phase angle errors versus the parameters uncertainty are shown for a PMU placement. Then, for different PMU placements, the values of parameters uncertainty that the state estimator is less biased are shown. The novelty of this analysis is that it finds the location of PMUs to have the state estimator less biased, considering different network parameters uncertainties.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".