Performance of Demodulation-Based Frequency Measurement Algorithms Used in Typical PMUs
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Bibliographic record
Abstract
This paper presents a method for evaluating the performance of demodulation-based frequency measurement algorithms in the presence of additive interfering sinusoids. Determination of the performance of amplitude measurement schemes under such conditions is straightforward once the frequency responses of the filters involved in the process are known, since the error induced by a single interfering tone is easily computed using the cascade algorithm's frequency response magnitude. This paper presents a similar method for predicting the worst error of frequency measurement schemes with respect to sinusoidal interference. Once acquainted with the proposed error prediction formula, the only difficulty in designing effective frequency measurement algorithms is the appropriate selection of output filters to achieve the specified performance. The method has been used successfully in designing frequency measurement algorithms currently used in Hydro-Que/spl acute/bec's special protection schemes.
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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.
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