Fault classification and location identification in a smart DN using ANN and AMI with real-time data

Autor: Muhammad Usama Usman, Juan Ospina, Md. Omar Faruque
Jazyk: angličtina
Rok vydání: 2019
Předmět:
neural nets
fault diagnosis
smart meters
sensitivity analysis
fault location
smart power grids
distribution networks
power system simulation
power distribution faults
uoc
event-driven data
sms
novel ann-based fault classification
balanced fault types
unbalanced fault types
drts
fault resistance
classification accuracy
smart dn
ami
real-time data
real-time fault classification
smart distribution network
advanced metering infrastructure
real-time testing
simulated power system model
real-time simulator
communication network
dnp3 protocol
data concentrator
utility operations centre
location identification method
digital real-time simulator
transfer control protocol
internet protocol
comprehensive sensitivity analysis
noise level
loading conditions
Engineering (General). Civil engineering (General)
TA1-2040
Zdroj: The Journal of Engineering (2019)
Druh dokumentu: article
ISSN: 2051-3305
DOI: 10.1049/joe.2019.0896
Popis: This paper presents a real-time fault classification and location identification method for a smart distribution network (DN) using artificial neural networks (ANNs) and advanced metering infrastructure (AMI). It also describes the development of a testbed for real-time testing of the proposed approach. The testbed consists of a simulated power system model [running on a digital real-time simulator (DRTS)] and AMI. The core parts of AMI are smart meters (SMs), a communication network (developed using DNP3 protocol over transfer control protocol/Internet protocol), data concentrator (DC), and a Utility Operations Centre (UOC). Event-driven data from SMs are collected in the DC and then fed to the UOC for being used as inputs for the novel ANN-based fault classification and location identification algorithm. On the basis of the data received, the algorithm can classify the fault type and locate it with high accuracy. Both balanced and unbalanced fault types are tested on different nodes and lines throughout a DN modelled in offline and on the DRTS. A comprehensive sensitivity analysis is performed to validate the effectiveness of the proposed method. Classification accuracy of over 99% is achieved when classifying all fault types, and above 95% accuracy is achieved when identifying the fault location.
Databáze: Directory of Open Access Journals
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