Pregled bibliografske jedinice broj: 886136
Detection of high impedance faults in power transmission network with nonlinear loads using artificial neural networks
Detection of high impedance faults in power transmission network with nonlinear loads using artificial neural networks // CIGRE International Colloquium on Lightning and Power Systems
Ljubljana, 2017. str. 1-7 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 886136 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Detection of high impedance faults in power transmission network with nonlinear loads using artificial neural networks
Autori
Teklić, Ljupko ; Filipović-Grčić, Božidar ; Pavić, Ivica ; Jerčić, Roko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
CIGRE International Colloquium on Lightning and Power Systems
/ - Ljubljana, 2017, 1-7
Skup
International Colloquium on Lightning and Power Systems
Mjesto i datum
Ljubljana, Slovenija, 18.09.2017. - 20.09.2017
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
High impedance faults ; nonlinear loads ; artificial neural networks ; power transmission system ; transmission lines ; relay protection
Sažetak
High impedance faults (HIF) represent one of the most difficult problems for fault detection in the power transmission networks. The main difficulty during the detection of HIFs is that a fault current is very low and therefore it is difficult to detect a fault through conventional protection devices such as distance or overcurrent relays. In case when transmission network contains a variety of connected nonlinear loads the detection of fault is even more challenging, because HIF currents and nonlinear load currents may have similar RMS values. Therefore, it is important to make a distinction between HIF current and rated current of nonlinear loads. This paper presents an approach for distinguishing HIFs from nonlinear load operation using artificial neural networks (ANNs). A part of the 110 kV transmission network is modelled in MATLAB Simulink to perform simulations of HIFs and nonlinear load operation. As input values ANNs use voltage and current waveforms in the frequency domain from both ends of the line. Pattern recognition neural network is used to make a distinction between HIF current and nonlinear load current in case of similar RMS values. Two cases of ANNs with different network sizes are compared considering the number of neurons in hidden layer. The proposed approach is successfully tested with actual measured current of single-phase electric locomotive with diode converters.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Projekti:
HRZZ-IP-2013-11-9299 - Razvoj naprednih visokonaponskih sustava primjenom novih informacijskih i komunikacijskih tehnologija (DAHVAT) (Uglešić, Ivo, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
