Irregularity Detection in Output Power of Distributed Energy Resources Using PMU Data Analytics in Smart Grids
Why this work is in the frame
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Bibliographic record
Abstract
The output power of distributed energy resources (DERs) may experience irregular fluctuations due to variations of renewable sources, which need to be monitored in order to reliably control the grid. This paper proposes a novel approach for centralized detection of such irregularities based on the time-series analysis of the data reported by phasor measurement units (PMUs). In this approach, a network controller constructs datasets of time-aligned real/reactive powers for different zones. The datasets are transformed into sequences of short-time local outlier probability (ST-LOP) that are analyzed to identify the DER events. The network controller estimates features such as the average duration and the similarity degree that is a measure of spatio-temporal correlation between the DER events. As a use case, event-triggered control of solar photovoltaic (PV) systems with energy storage devices is investigated. The simulation results for the IEEE 123-bus network corroborate the effectiveness of the developed analytics for detection and mitigation of ramp-rate solar power fluctuations. Smart microgrids and active distribution networks can employ the developed analytics to improve a range of diagnostic and control functionalities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it