Neural Network Based Fault Location in Power Distribution System
Bibliographic record
Abstract
Locating the fault in the power distribution system is a tedious process that involves manually searching along the line. While some methods rely on complete system parameters or centralized data processing, it has become difficult due to the bidirectional power flow in the active distribution system. This paper proposes a framework consisting of three steps: detecting, classifying, and locating the fault in the active distribution system. The proposed method reduces the search area by using the sending-end sample value of the line current. It can be implemented in a distributed form without knowledge of system parameters using pole-mounted data recording meters and relaying the approximate fault location and type to the control center. In the first step, the fault is detected using an isolation forest. Then, the fault is classified by the support vector machine, and finally, the fault is located using an adaptive weight convolutional neural network (CNN). The CNN weights are modified according to fault type information that uses time-frequency information extracted by a continuous wavelet transform (CWT). The proposed framework is tested using an IEEE 13-node test feeder with a solar photovoltaic system. Different training parameters for the CNN are also tested to analyze the proposed framework
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".