Centralized Disturbance Detection in Smart Microgrids With Noisy and Intermittent Synchrophasor Data
Bibliographic record
Abstract
Microgrids are prone to network-wide disturbances such as voltage and frequency deviations. Detection of disturbances by a microgrid central controller is therefore necessary for improving the network operation. Motivated by this application, this paper presents a new structure for the centralized detection of disturbances with noisy synchrophasor data and packet delay/dropouts. We build the proposed structure starting from the analysis of noise-delay tradeoff in synchrophasor networks and developing a new phasor data concentrator for compensation of data losses. The statistical performance metrics of the disturbance detector are numerically evaluated in the case of islanding detection, corroborating that the centralized detector counteracts the measurement noise and lowers the detection time. Numerical results show that the proposed structure significantly mitigates the probability of false detection. Moreover, it can achieve the lower bound of average detection time in a wide range of packet drop rates. This paper is useful to network designers who need to employ data acquisition systems for reliable and robust microgrid control applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".