CIM compliant power system model exchange for Indian power grid control centers
Bibliographic record
Abstract
CIM is widely adopted by many power utilities since it offers interoperability through standard information models. This paper discusses the CIM based modeling aspects for representing a regional power system network of Indian power grid in CIM format. Two control center applications, namely network model exchange and state estimation are considered. The CIM based modeling approach addresses three significant problems faced by utilities such as 1) seamless exchange of network models, 2) representation of boundary elements which are typically modeled in the networks of both the neighboring control areas, and 3) exchange of state variables obtained from state estimation application. The model authority sets (MAS) are defined for handling of boundary elements that occur at the regional network boundaries, wherein the globally unique reference IDs are allotted for unique identification of power system resources. The MAS approach supports easy merging of multiple regional CIM network models into a national CIM network model. The CIM profiles for network model exchange and state estimation applications are defined. Consequently, CIM/RDF/XML message payloads for the applications are generated for western region 400 kV network of Indian power grid.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".