Non-linear high impedance fault distance estimation in power distribution systems: A continually online-trained neural network approach

2018 ◽  
Vol 157 ◽  
pp. 20-28 ◽  
Author(s):  
Patrick E. Farias ◽  
Adriano Peres de Morais ◽  
Jean Pereira Rossini ◽  
Ghendy Cardoso
DYNA ◽  
2015 ◽  
Vol 82 (192) ◽  
pp. 141-149 ◽  
Author(s):  
Andres Felipe Panesso-Hernández ◽  
Juan Mora-Flórez ◽  
Sandra Pérez-Londoño

<p>The impedance-based approaches for fault location in power distribution systems determine a faulted line section. Next, these require of the estimation of the voltages and currents at one or both section line ends to exactly determine the fault location. It is a challenge because in most of the power distribution systems, measurements are only available at the main substation.  This document presents a modeling proposal of the power distribution system and an easy implementation method to estimate the voltages and currents at the faulted line section, using the measurements at the main substation, the line, load, transformer parameters and other serial and shunt connected devices and the power system topology. The approach here proposed is tested using a fault locator based on superimposed components, where the distance estimation error is lower than 1.5% in all of the cases. </p>


2020 ◽  
Author(s):  
Douglas Pinto Sampaio Gomes ◽  
Cagil Ozansoy

High-impedance faults in power distribution systems is a lasting problem with decades of steady investigation. Due to the complexity of the problem, the field can also be challenging to navigate. Although there exist surveys of the field in the literature, it is not easy to find a comprehensive contextualization of how and when the field developments unfolded. This paper presents the historical narrative of the progress and developments based on the most cited papers since the inception of the field. The accounts are not limited to archaic and obsolete works. They are all contextualized from the seminal papers to contemporary methods and related technology. Quantitative figures on the survey of the methods and relevant knowledge gaps are also discussed at the closing of the paper.


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