Hybrid classifier for fault location in activedistribution networks

Sadegh Jamali; Alireza Bahmanyar; Siavash Ranjbar

doi:10.1186/s41601-020-00162-y

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Hybrid classifier for fault location in activedistribution networks

Original Research | 更新时间：2026-09-30

- Hybrid classifier for fault location in activedistribution networks
- Protection and Control of Modern Power Systems Vol. 5, Issue 2, Pages: 1-9(2020)
- 作者机构：
  
  Centre of Excellence for Power System Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran,Iran
- 作者简介：
- 基金信息：
- DOI：10.1186/s41601-020-00162-y
  CLC：
- Published：01 April 2020
- 稿件说明：
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Sadegh Jamali, Alireza Bahmanyar, Siavash Ranjbar. Hybrid classifier for fault location in activedistribution networks[J]. Protection and Control of Modern Power Systems, 2020, 5(2): 1-9.
DOI：

Sadegh Jamali, Alireza Bahmanyar, Siavash Ranjbar. Hybrid classifier for fault location in activedistribution networks[J]. Protection and Control of Modern Power Systems, 2020, 5(2): 1-9. DOI： 10.1186/s41601-020-00162-y.

摘要

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation (DG) and microgrids. The method uses the voltage and current data from the measurement points at the main substation

and the connection points of DG and microgrids. The data is used in a single feedforward artificial neural network (ANN) to estimate the distances to fault from all the measuring points. A k-nearest neighbors (KNN) classifier then interprets the ANN outputs and estimates a single fault location. Simulation results validate the accuracy of the fault location method under different fault conditions including fault types

fault points

and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.

Abstract

fault points

and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.

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