Title :
Wide area power system protection using a learning vector quantization network
Author :
Venayagamoorthy, Ganesh K. ; Zarghami, Mahyar
Author_Institution :
Real-Time Power & Intelligent Syst. Lab., Missouri-Rolla Univ.
Abstract :
This paper presents a wide area monitoring and protection technique based on a learning vector quantization (LVQ) neural network. Phasor measurements of the power network buses are monitored continuously by a LVQ network in order to alert the control room operators of possible faults. The proposed scheme could be used in a wide area monitored network to provide remedial action when primary local protection schemes for transmission lines fail to function. This technique could also be extended to the actuation of the secondary protection schemes, hence, preserving the integrity of the power network especially when the faults are spreading over a wide area network to the other areas of the system. The scheme has been applied to a two-area power system in this paper and the LVQ results show that it is a promising scheme for system protection against partial or total blackouts or brown outs
Keywords :
neural nets; phase measurement; power system protection; power system stability; vector quantisation; wide area networks; LVQ network; learning vector quantization neural network; phasor measurement; power network bus; transmission line; wide area power system protection; Circuit faults; Condition monitoring; Neural networks; Phasor measurement units; Power system faults; Power system measurements; Power system protection; Power system relaying; Power transmission lines; Vector quantization;
Conference_Titel :
Intelligent Systems Application to Power Systems, 2005. Proceedings of the 13th International Conference on
Conference_Location :
Arlington, VA
Print_ISBN :
1-59975-174-7
DOI :
10.1109/ISAP.2005.1599286