Adaptive Dynamic State Estimation in Power Systems With Real-Time Y-Bus Matrix Estimation: A Step Toward Greater Practicality
Bibliographic record
Abstract
Dynamic State Estimation (DSE) has become pivotal in power system regulation and real-time contingency analysis, thanks to advancements in Phasor Measurement Units (PMUs) and Wide-Area Measurement Systems (WAMS). Traditionally, DSE relies on accurate, up-to-date information regarding system topology and loads. Maintaining precise estimations of these dynamic parameters is challenging and crucial for effective control and protection actions within power grids. This paper addresses the limitations of traditional DSE methods by relaxing the assumption that the admittance (i.e., Y-bus) matrix must be provided as an input at every time step. Instead, an adaptive variable forgetting-factor Recursive Least Squares (RLS) estimator is proposed for real-time Y-bus matrix estimation. This innovative approach leverages the inverse power flow equations to dynamically estimate the reduced Y-bus while simultaneously performing state estimation. To enhance accuracy and responsiveness, the estimator is integrated with the Gauss-Newton Variable Forgetting Factor (GN-VFF), allowing for precise adjustments and efficient tracking of changes. Implemented within a batch-mode regression-based Extended Kalman Filter (EKF), the GN-VFF-enhanced Y-bus estimator bridges the gap between theoretical assumptions and practical implementation. The effectiveness of this approach is validated through various scenarios on the IEEE 14-bus test system, demonstrating its potential to improve the practicality and performance of DSE in power systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".