Identification Method for Wideband Oscillation Parameters Caused by Grid-Forming Renewable Energy Sources Based on Multiple Matching Synchrosqueezing Transformation
Bibliographic record
Abstract
The oscillation problem has emerged as one of the critical challenges confronting emerging power systems, particularly with the increasing penetration of grid-forming renewable energy sources. This trend can lead to the coexistence of multiple oscillation modes across a wide frequency range. To enhance the safety and stability of power systems, this paper proposes a wideband oscillation parameter identification method based on the multiple matching synchrosqueezing transform (MMSST), addressing the limitations of traditional time–frequency analysis techniques in accurately separating and extracting oscillation components during wideband parameter identification. The method first applies MMSST to decompose the measured oscillation signal into a set of intrinsic mode functions (IMFs). Subsequently, the Hilbert transform is applied to each IMF to extract the instantaneous frequency, amplitude, and initial phase, thereby achieving precise parameter identification of the oscillation signal. The validation study results demonstrate that the MMSST algorithm outperforms the empirical mode decomposition (EMD) and variational mode decomposition (VMD) algorithms in accurately extracting individual oscillation components and estimating their dynamic characteristics. Additionally, the proposed method achieves superior performance in terms of both accuracy and robustness when compared to the EMD and VMD algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".