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Record W2115910105 · doi:10.1109/pes.2006.1709589

Topological observability analysis using heuristic rule based expert system

2006· article· en· W2115910105 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2006 IEEE Power Engineering Society General Meeting · 2006
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsObservabilityHeuristicSpanning treeObservableMinimum spanning treeComputer scienceGraphElectric power systemGraph theoryExpert systemTree (set theory)MathematicsMathematical optimizationTopology (electrical circuits)AlgorithmTheoretical computer sciencePower (physics)Discrete mathematicsArtificial intelligenceCombinatoricsApplied mathematics

Abstract

fetched live from OpenAlex

This paper presents a novel approach for topological observability analysis using heuristic rule based expert system. The observability problem is split in P-delta observability and Q-V observability by P-delta/Q-V decouple characteristic of power systems. A heuristic rule based expert system is developed for finding the existence of an observable spanning tree for P-delta measurement graph and Q-V measurement graph. This expert system finds the existence of an observable spanning tree in measurement graph on the basis of heuristic rules, directly without making a spanning tree. Inference in this approach is done by the process of chaining through rules until a conclusion about the observability or unobservability is reached. The proposed heuristic rule based expert system has been tested on the standard IEEE 5 bus and 14 bus test systems and an 87 bus real power system, which is a part of Northern grid network of India. Results obtained are presented for illustration

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.444
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.215
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it