Bibliographic record
Abstract
The role that a Smart Substation Automation System (SSAS) plays as a bridge between a power system and the Smart Grid (SG) is very important for the stable application of a SG in the field. A variety of SG functions affect the power system, and as the number of these functions increases, the influence of the SG in a power system increases as well. Therefore, SSAS will become increasingly important for the application of SG in power systems. Most reported work on this subject has been on the implementation of conventional protection and automation schemes in the IEC 61850 environment. However, these studies did not indicate the importance of a SSAS or define a SSAS in the context of a SG; a conceptually ambiguously SSAS formed the basis on which their algorithms and systems were proposed. In order to clarify this ambiguity, this paper proposes both a definition and a concept of SSAS. This paper proposes the concept from two perspectives; the first perspective addresses the change required in a powerful new system environment that considers SG. The second perspective involves the use of smart functions that enhance the operational efficiency of a SSAS. The concept of the system environment takes into consideration the type of communication network, network topology, protocol, cyber security, IEDs and other automation devices, and the influence of Distributed Generation (DG). In addition, the concept of smart functions takes into account optimized operation, protection, restoration, a distributed control operation, and microgrid operation.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".