A N4SID-Based Strategy to Estimate the Parameters of Disturbances in Power Systems
Bibliographic record
Abstract
A blind identification method based on Subspace State Space System IDentification (N4SID) is proposed to identify the parameters of disturbances, such as electromechanical modes and harmonics, in a power system. The challenge, however, is how to identify the disturbances without the knowledge of systems' inputs. The disturbances are only measured by Phasor Measurement Units (PMUs) in the power system as signals representing the system's outputs; the system's parameters and inputs have to be estimated only from the measured outputs. This leads to a blind identification problem; therefore, N4SID, categorized as blind identification, is employed to address the problem. To solve the problem, the output data is formulated in the N4SID terminology, system matrices are calculated in 6 steps, and finally, the forward stochastic model of the system is derived. The proposed method has some advantages such as robustness and no need to know the parameters of the power system for design. To evaluate the performance of the proposed method, simulation studies are carried out on two practical power systems. The simulations show that the proposed method has a desirable performance.
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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".