Cubature Particle Filtering Approach for State Estimation in Electrical Distribution Systems
Bibliographic record
Abstract
Motivated by the increasing need for robust and accurate state estimators, capable of capturing the dynamics of system states and suitable for large-scale distribution networks with a lack of sensors, we introduce a state estimator based on a distributed approach. The proposed estimator technique is based on a combination of the Particle Filter (PF) and the CKF, which yields a Cubature Particle Filter (CPF). This technique employs a PF with the proposal distribution provided by the CKF. Unlike the other types of filters, the PF is a non-Gaussian algorithm from which a true posterior distribution of the estimated states can be obtained. This paper also provides a comparison study between the above mentioned algorithm and the latest algorithms available in the literature. The proposed algorithm were implemented in MATLAB to verify their theoretical expectations. To validate their robustness and accuracy, the proposed methods were tested and verified using a large range of customer loads with 30% uncertainty on a connected IEEE 123-bus system.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".