Distributed monitoring and centralized forecasting network for DG-connected distribution systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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