k-NN based fault detection andclassification methods for powertransmission systems

Aida Asadi Majd; Haidar Samet; Teymoor Ghanbari

doi:10.1186/s41601-017-0063-z

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k-NN based fault detection andclassification methods for powertransmission systems

Contents | 更新时间：2026-06-25

- k-NN based fault detection andclassification methods for powertransmission systems
- Protection and Control of Modern Power Systems Vol. 2, Issue 4, (2017)
- 作者机构：
- 作者简介：
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- DOI：10.1186/s41601-017-0063-z
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- Published：2017
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Aida Asadi Majd, Haidar Samet, Teymoor Ghanbari. k-NN based fault detection andclassification methods for powertransmission systems[J]. Protection and Control of Modern Power Systems, 2017, 2(4).
DOI：

Aida Asadi Majd, Haidar Samet, Teymoor Ghanbari. k-NN based fault detection andclassification methods for powertransmission systems[J]. Protection and Control of Modern Power Systems, 2017, 2(4). DOI： 10.1186/s41601-017-0063-z.

摘要

This paper deals with two new methods

based on k-NN algorithm

for fault detection and classification in distance protection. In these methods

by finding the distance between each sample and its fifth nearest neighbor in a predefault window

the fault occurrence time and the faulty phases are determined. The maximum value of the distances in case of detection and classification procedures is compared with pre-defined threshold values. The main advantages of these methods are: simplicity

low calculation burden

acceptable accuracy

and speed. The performance of the proposed scheme is tested on a typical system in MATLAB Simulink. Various possible fault types in different fault resistances

fault inception angles

fault locations

short circuit levels

X/R ratios

source load angles are simulated. In addition

the performance of similar six well-known classification techniques is compared with the proposed classification method using plenty of simulation data.

Abstract

This paper deals with two new methods

based on k-NN algorithm

for fault detection and classification in distance protection. In these methods

by finding the distance between each sample and its fifth nearest neighbor in a predefault window

low calculation burden

acceptable accuracy

and speed. The performance of the proposed scheme is tested on a typical system in MATLAB Simulink. Various possible fault types in different fault resistances

fault inception angles

fault locations

short circuit levels

X/R ratios

source load angles are simulated. In addition

the performance of similar six well-known classification techniques is compared with the proposed classification method using plenty of simulation data.

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