Artificial neural network approach for locating faults in power transmission system

Autor:	Ljupko Teklic, Ivan Pavicic, Bozidar Filipovic-Grcic
Rok vydání:	2013
Předmět:	Stuck-at fault Computer Science::Hardware Architecture Electric power system Power transmission Engineering Electric power transmission business.industry Fault coverage Electronic engineering Fault model business Fault (power engineering) Fault indicator
Zdroj:	EUROCON
DOI:	10.1109/eurocon.2013.6625165
Popis:	This paper presents fault location recognition in transmission power system using artificial neural network (ANN). Single phase short circuit on 110 kV transmission line fed from both ends was analysed with various fault impedances, since it is the most common fault in power system. Load flow and short circuit calculations were performed with EMTP-RV software. Calculation results including currents and voltages at both line ends were used for training ANN in Matlab in order to obtain correct fault location and fault impedance, even for those cases that ANN has never encountered before. The network was trained with back propagation algorithm. Test results show that this approach provides robust and accurate location of faults for a variety of power system operating conditions and gives an accurate fault impedance assessment.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5a279f8db5c84bcf305b3ef7bff503fd https://doi.org/10.1109/eurocon.2013.6625165 Zobrazit plný text záznamu