Title :
Fuzzy-neuro approach to fault classification for transmission line protection
Author :
Wang, Huisheng ; Keerthipala, W.W.L.
Author_Institution :
Nanyang Technol. Univ., Singapore
fDate :
10/1/1998 12:00:00 AM
Abstract :
This paper presents a new approach to real-time fault detection and classification in power transmission systems by using fuzzy-neuro techniques. The integration with neural network technology enhances fuzzy logic systems on learning capabilities. The symmetrical components in combination with three line currents are utilized to detect fault types such as single line-to ground, line-to-line, double line-to-ground and three line-to-ground, and then to define the faulty line. Computer simulation results are shown in this paper and they indicate this approach can be used as an effective tool for high speed digital relaying, as the correct detection is achieved in less than 10 ms
Keywords :
control system analysis computing; control system synthesis; fault location; fuzzy control; fuzzy neural nets; learning (artificial intelligence); neurocontrollers; power system analysis computing; power system control; power system protection; power system relaying; power transmission lines; real-time systems; relay protection; transmission networks; computer simulation; double line-to-ground faults; fault classification; fuzzy logic systems; fuzzy-neuro approach; high-speed digital relaying; learning capabilities; line-to-line faults; neural network technology; power system protection automation; power transmission line protection; single line-to ground faults; symmetrical components; three line-to-ground faults; Computer simulation; Digital relays; Electrical fault detection; Fault detection; Fuzzy logic; Neural networks; Power transmission; Power transmission lines; Real time systems; Transmission lines;
Journal_Title :
Power Delivery, IEEE Transactions on