Intelligent Signal Processing and Coordination for the Adaptive Smart Grid: An Overview of Data-Driven Grid Management
Bibliographic record
Abstract
In today's era of the Internet of Things (IoT), the amalgamation of information and communication technologies with actuating devices has reached all corners of the modern world. In the context of critical infrastructures, such as the power grid, this cyberphysical transformation has permeated all system levels as evident in devices ranging from crucial operational components (e.g., generators) and advanced sensors [e.g., phasor measurement units (PMUs) and programmable controllers], to consumer-centric devices [smart meters, electric vehicles (EVs), and smart appliances]. These extended cyberphysical functionalities have opened up signal processing opportunities that can be harnessed to empower actuating devices to adaptively and synergistically acquire data, conduct analytics, and respond to system and environmental changes for better power-grid operations. In this article, we demonstrate how a hierarchical signal processing and actuation framework can enable the tractable all-encompassing coordination of thousands of actuating power entities to maintain efficient operations while accounting for physical infrastructure limits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".