Enhancing traditional process SCADA and historians for industrial & commercial power systems with energy (via IEC 61850)
Bibliographic record
Abstract
Coordination of large scale distributed measurement and control systems, such as Supervisory Control and Data Acquisition (SCADA) and other process control system implementations, require reliable and robust networks that can connect thousands of remote devices from multiple remote locations. Additionally, many heavy industries have ever increasing demands of their infrastructure to handle more network services and deliver a full spectrum of control and monitoring of thousands and even tens of thousands of points within their global business operations. This paper will outline a new novel method of providing a convergent use of the IEC 61850 protocol, within process control networks, to provide a more deterministic type of process control, monitoring and energy management. The benefits of real-time archiving of process values, process messages, Intelligent Electronic Device (lED) status and alarm data along with process batch data can be achieved in an advancement of traditional historian based systems. This new approach, providing traditional downtime reports and alarm summaries for maintenance purposes along with the added benefits of providing the opportunity for highly accurate and time synchronized energy consumption and energy balance data along with efficiency reports of various different production or process operations.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".