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ARTIFICIAL NEURAL-NETWORK POWER-SYSTEM STABILIZERS IN MULTIMACHINE POWER-SYSTEM ENVIRONMENT
被引:40
|作者:
ZHANG, Y
MALIK, OP
CHEN, GP
机构:
[1] Department of Electrical and Computer Engineering, The University of Calgary, Calgary, Alberta
关键词:
POWER SYSTEM STABILIZER;
ARTIFICIAL NEURAL NETWORK;
INVERSE PLANT;
MULTILAYER NETWORK ERROR BACKPROPAGATION;
MULTIMACHINE;
MULTI-MADE OSCILLATION;
D O I:
10.1109/60.372580
中图分类号:
TE [石油、天然气工业];
TK [能源与动力工程];
学科分类号:
0807 ;
0820 ;
摘要:
Effectiveness of an artificial neural network (ANN), functioning as a power system stabilizer (PSS), in damping multi-mode oscillations in a Ave-machine power system environment is investigated in this paper. Accelerating power of the generating unit is used as the input to the ANN PSS. The proposed ANN PSS using a multilayer neural network with error-backpropagation training method was trained over the full working range of the generating unit with a large variety of disturbances. The ANN was trained to memorize the reverse input/output mapping of the synchronous machine. Results show that the proposed ANN PSS can provide good damping for both local and inter-area modes of oscillations.
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页码:147 / 155
页数:9
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