Power Oscillation Localization: A Synchrophasor Based Adaptive Vold-Kalman Filtering Energy Flow
Bibliographic record
Abstract
With widespread deployments of phasor measurement units (PMUs) in power systems, the localization of power oscillations using synchrophasor measurements has become feasible. However, the classical method for source localization, known as the energy-based method, is significantly impacted by noise and other irrelevant frequency components, which are common in synchrophasor measurements. In response to this challenge, the paper proposes an adaptive Vold-Kalman filtering-based Energy method (A-VKF-Energy). Initially, the Fast Fourier Transform is employed to identify oscillation frequency in active power, offering a reference for subsequent component extraction. The Adaptive Vold-Kalman filtering is then utilized to extract oscillation components from PMU data, which are subsequently employed in computing dissipating energy for each branch. Moreover, the slope ratio of the energy is employed as an indicator of the energy flow direction in the power system, automating the process of determining the source of oscillations. The superior performance of adaptive Vold-Kalman filtering in frequency coupling is verified by simulated experiments. Furthermore, simulation using WECC 179 test case data and actual experiments using a real oscillation event are carried out to verify the effectiveness of proposed method. The results reveal that A-VKF-Energy method can successfully identify oscillation sources.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".