Towards a Power System Fault Classification System: A Rough Neurocomputing
Bibliographic record
Abstract
A rough neurocomputing approach to classifying power system faults is presented in this paper. Preprocessing fault data entails discretization of power system fault data obtained from the Transcan Recording System at Manitoba Hydro. An approach to discretizing power system fault data is briefly described in this article. After preprocessing, rough set methods are used to prepare fault decision tables and generate fault classification rules. Each condition vector contain values of attributes for a new power system fault becomes input to mixture of rough set-based expert networks, where each the processing performed by each “expert ” is tailored to a particular fault type. A collection of rough expert networks are connected to what is known as gating network that selects the output of competing expert networks (the winner) as the output of the network. That is, the gating network implements derived fault classification rules and selects the expert network with the best classification (i.e., highest probability of correctly classified fault). The architecture of such a network is extensible inasmuch as a new rough expert network can be added to accommodate the discovery of a new type of power system fault. The basic architecture of a new fault neural classification system is presented. The contribution of this paper is an overview of the basic building blocks in a rough set-based power system fault classification system. 1.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".