Title :
Transient stability assessment for single-machine power systems using neural networks
Author :
Wong, Kit Po ; Ta, Nhi Phuoc ; Attikiouzel, Yianni
Author_Institution :
Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia
Abstract :
The use of back-propagation neural networks for fast and efficient determination of the stable and unstable modes of operation of power systems is proposed. The transient response quantities selected as input features for training the neural network and for carrying out the assessment using the neural network are quantities which can either be calculated easily or be measured. The training process for the neural network requires very low computing time. When applied to a single-generator system, the neural network can assess the transient stability of the system accurately for a symmetrical fault on any point along the external circuit and for the range of normal prefault loading conditions. The authors also developed a back-propagation neural network for the estimation of critical fault clearing time. The authors describe the neural network configurations adopted and their use in stability assessment. Results obtained by applying the neural networks to a single-machine system are presented
Keywords :
neural nets; power system analysis computing; stability; transient response; back-propagation neural networks; critical fault clearing time; single-machine power systems; stability assessment; symmetrical fault; training; transient response; transient stability; Circuit faults; Equations; Neural networks; Power system analysis computing; Power system faults; Power system interconnection; Power system modeling; Power system stability; Power system transients; Rotors;
Conference_Titel :
Computer and Communication Systems, 1990. IEEE TENCON'90., 1990 IEEE Region 10 Conference on
Print_ISBN :
0-87942-556-3
DOI :
10.1109/TENCON.1990.152560