High-Impedance Fault Detection in Distribution Networks With Use of Wavelet-Based Algorithm

2006 ◽  
Vol 21 (4) ◽  
pp. 1793-1802 ◽  
Author(s):  
M.R. Lukowicz ◽  
L. Kang
Keyword(s):  
Fault Detection ◽  
Distribution Networks ◽  
High Impedance ◽  
High Impedance Fault

scholarly journals High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2331 ◽  
Author(s):  
Saeid Khavari ◽  
Rahman Dashti ◽  
Hamid Reza Shaker ◽  
Athila Santos
Keyword(s):  
Fault Detection ◽  
Power Distribution ◽  
Fault Location ◽  
Distribution Networks ◽  
Discrete Wavelet ◽  
Underground Cables ◽  
Overhead Lines ◽  
High Impedance ◽  
Data Loggers ◽  
High Impedance Fault

Power distribution networks are vulnerable to different faults, which compromise the grid performance and need to be managed effectively. Automatic and accurate fault detection and location are key components of effective fault management. This paper proposes a new framework for fault detection and location for smart distribution networks that are equipped with data loggers. The framework supports networks with mixed overhead lines and underground cables. The proposed framework consists of area detection, faulty section identification, and high impedance fault location. Firstly, the faulty zone and section are detected based on the operation of over-current relays and digital fault recorders. Then, by comparing the recorded traveling times at both ends of lines, which are related to the protection zone, the faulty line is identified. In the last step, the location of the fault is estimated based on discrete wavelet transform. The proposed method is tested on a 20 kV 13 node network, which is composed of overhead lines and underground cables. The method is tested in both balanced and unbalanced configurations. The obtained results confirm the advantages of the proposed method compared with the current state-of-the art.

High impedance fault detection method for distribution networks under non-linear conditions

2021 ◽  
Vol 131 ◽  
pp. 107041
Author(s):  
Érica Mangueira Lima ◽  
Rodrigo de Almeida Coelho ◽  
Núbia Silva Dantas Brito ◽  
Benemar Alencar de Souza
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Distribution Networks ◽  
High Impedance ◽  
Non Linear ◽  
High Impedance Fault

scholarly journals Characterization of 400 Volt High Impedance Fault with Current and Magnetic Field Measurements

2021 ◽  
Author(s):  
Anwarul Islam Sifat ◽  
FJ Stevens McFadden ◽  
Joseph Bailey ◽  
Ramesh Rayudu ◽  
A Hunze
Keyword(s):  
Magnetic Field ◽  
Fault Detection ◽  
Physical Simulation ◽  
Health And Safety ◽  
Distribution Networks ◽  
Test Facility ◽  
High Impedance ◽  
High Impedance Fault ◽  
Gmr Sensors ◽  
Tree Branch

Electrical faults, which can occur at all voltage levels in an electricity supply system, are a health and safety risk. Multi-branch distribution networks represent a significant ongoing challenge for fault detection, with the greatest challenge being high impedance fault (HIF) detection. To date, research has focused at higher voltage levels and fault monitoring sensors have traditionally only been installed in limited locations within the higher voltage networks. The main contributions of this paper are to characterize a high impedance fault (HIF) involving a tree branch and to experimentally verify the feasibility of giant magneto-resistive (GMR) sensors, located distant from the overhead lines, for fault detection. In a purpose-built 400 V physical simulation test facility, we have collected current and magnetic field data during HIF involving a tree branch. We have identified new characteristics in the early stages of this fault type, which persist for a reasonable length of time but are only observable when suitable signal processing techniques are applied. New detection schemes will, therefore, need to be developed to detect such faults. GMR sensors were found to be suitable for observing the characteristics of HIF, validating their potential use for fault detection. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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