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Mathematical morphology-based local fault detection in DC Microgrid clusters

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posted on 11.01.2021, 11:37 by Navid Bayati, Hamid Reza Baghaee, Amin Hajizadeh, Mohsen Soltani, Zhengyu LinZhengyu Lin
A new local current-based fast high impedance fault (HIF) detection scheme for DC microgrid clusters using mathematical morphology (MM) is proposed in this paper. The proposed strategy consists of two MM based parts. The first part is MM erosion filtering to extract the current signals and its components to extract the differential feature vector. The second part is MM regional maxima, for defining a determinative value to detect faults in a line segment by the lowest possible time. This scheme also uses local measured values to eliminate the need for communication channels, which provide a low cost, reliable, and fast fault detection method for DC microgrid clusters. Moreover, to provide an accurate HIF detection method, the accurate HIF model in DC systems is presented and used in the proposed method. For demonstrating the efficiency, authenticity, and compatibility of the proposed method, digital time-domain simulations are carried out in MATLAB/Simulink environment under different scenarios such as overload, noise, low and HIFs to distinguish between overloads and HIFs, and the results are compared with several reported algorithms. The obtained simulation results are verified by experimental tests, which validate the proposed strategy's accuracy and speed under different conditions.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Electric Power Systems Research

Volume

192

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Electric Power Systems Research and the definitive published version is available at https://doi.org/10.1016/j.epsr.2020.106981.

Acceptance date

23/11/2020

Publication date

2020-12-01

Copyright date

2020

ISSN

0378-7796

Language

en

Depositor

Dr Zhengyu Lin. Deposit date: 6 January 2021

Article number

106981

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