Distributed monitoring and centralized forecasting network for DG-connected distribution systems
Bibliographic record
Abstract
Dispersed generations (DGs) from renewable energy resources are becoming popular and start to show benefits, but their connection to distribution systems brings operation challenges and supply uncertainty that must be carefully monitored and forecasted to provide data for correct controls of the systems. This paper proposes a distributed monitoring and centralized forecasting strategy for distribution systems connected with DGs. The paper illustrates functional implementations for the distributed monitoring and centralized forecasting operations utilizing high-speed digital signal processing (DSP) technology and network classified data transmission (CDT) algorithm. The design of a new DSP-based network monitoring architecture is provided. This architecture is fault tolerant and has features from classical cascading, star, and ring architectures. The paper presents the CDT-based real-time data acquisition and DSP-based data post-processing strategy, design, and implementation in three levels: Cell units for monitoring feeder-node circuits including DG circuit connected on the feeder, Domain unit for a section of the distribution circuit, and Station unit for the complete distribution circuit.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".