Fault location in distribution systems using mathematical analysis and support vector machine / Sophi Shilpa Gururajapathy
Bibliographic record
Abstract
Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human error. These phenomena affect the system reliability and results in expensive repairs, damaged work in process, lost productivity and power loss to customers. Due to this, various intelligent methods have been developed to locate fault in distribution system. However, fault location using intelligent methods is challenging since it requires training data for processing. The training data is commonly created by simulation, which is time consuming. Therefore, in this work, a fault location method based on previous work is proposed using limited simulation data. The existing method was improved by estimating voltage sag data using support vector machine, thus limiting the simulated data. Faulty section is identified by comparing the actual voltage sag data with the simulated and estimated voltage sag data. An improved ranking and Euclidean distance approach for fault distance is also presented. A method using SVM is also proposed to identify the faulty phase, fault type, faulty section and fault distance. By having these features, a more accurate and effective fault location can be obtained. The method identifies faulty phase and fault type using support vector classification analysis. Meanwhile, the faulty section and the fault distance are identified using support vector regression analysis. The effectiveness of the proposed method was tested on an actual TNB distribution network from Malaysia and SaskPower distribution network from Canada. The test cases were conducted for all types of fault and for various fault resistances. The test results have proven the effectiveness of the proposed method in locating fault under various conditions. It has shown improvement over the existing trigonometric methods in locating different types of faults and may serve as an alternative technique for estimating fault location in distribution networks.
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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) | 0.001 | 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".