Title :
An artificial neural network application to distance protection [of power systems]
Author :
Qi, W. ; Swift, G.W. ; McLaren, P.G. ; Castro, A.V.
Author_Institution :
Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada
Abstract :
An application of artificial neural networks (ANNs) to power system distance protection is presented in this paper. A neural network was trained by data from simulation of a simple power system under load and fault conditions, tested by data with different system conditions, and finally run for faults along the whole power line. The research was concentrated on creating more selective arcing fault detection, especially for radial distribution lines where arc resistance can be a significant part of the zero sequence impedance. A nonlinear arcing resistance model was used to provide data and a new operating characteristic was devised. The prospective ANN distance relay showed very good performance in detecting a single-line-to-ground fault with nonlinear arcing resistance along the whole transmission line
Keywords :
arcs (electric); electrical faults; learning (artificial intelligence); neurocontrollers; power system control; power system protection; application; arc resistance; artificial neural network; distance protection; fault conditions; load conditions; nonlinear arcing resistance model; operating characteristic; power line; power systems; radial distribution lines; selective arcing fault detection; single-line-to-ground fault; transmission line; zero sequence impedance; Artificial neural networks; Electrical fault detection; Fault detection; Impedance; Power system faults; Power system modeling; Power system protection; Power system simulation; Protective relaying; System testing;
Conference_Titel :
Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP '96., International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-3115-X
DOI :
10.1109/ISAP.1996.501073