APPLICATION OF CASE BASED REASONING (CBR) METHOD IN DISTRIBUTION TRAFO DAMAGE EXPERT SYSTEM AT PLN UP3 BINJAI
Bibliographic record
Abstract
In the world of electricity, transformers are widely used, both in the field of electric power and electronics. The use of transformers in the electric power system is used for each of its needs, for example the need for high voltage in the delivery of electrical power over long distances. Electrical equipment transformers are expensive and are very vital equipment. If the transformer is disturbed or in an abnormal condition, there may be an unexpected temporary cessation of electricity distribution and will cause losses for PLN and consumers who use electricity by disrupting the activities carried out. The Case Based Reasoning (CBR) method is a case-based reasoning system that uses previous experiences or cases so that they can solve new problems or cases. There are several stages in the case based reasoning method, including retrieve, reuse, revise, and retain. Case-based Reasoning (CBR) collects previous cases that are similar to the new problem and tries to modify the solution to fit the new case. The basic idea of Case-Based reasoning is the assumption that similar problems have similar solutions. While this assumption is not always true, it does depend on many practical domains. Where the previous case experienced an overvoltage transformer damage, using the CBR method will look for similarities to the previous case with existing characteristics, the CBR method will look for the same case on the existing Knowladge so that the damage is quickly detected. From the characteristics of the damage that is on the transformer, it is easy to diagnose transformer damage using the CBR method and the results of the CBR method will find a ranking that is almost similar to the existing case.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".