DocumentCode :
1948719
Title :
A practical neural network approach for power generation automation
Author :
Moghavvemi, Mahmoud ; Yang, S.S. ; Kashem, M.A.
Author_Institution :
Dept. of Electr. Eng., Malaya Univ., Kuala Lumpur, Malaysia
Volume :
1
fYear :
1998
fDate :
3-5 Mar 1998
Firstpage :
305
Abstract :
This paper presents a practical artificial neural network (ANN) based technique for the automation of power generation scheduling based on the consumer´s load profile. A multi-layered neural network with backpropagation learning algorithm is used to predict the required power generation to fulfill the consumer´s demands. The proposed technique has been applied to a typical co-generation power plant of 4×8 MW rating. Test results indicates that the ANN model can automatically perform generator scheduling accurately
Keywords :
backpropagation; cogeneration; multilayer perceptrons; power engineering computing; power station control; scheduling; thermal power stations; 8 MW; artificial neural network; backpropagation learning algorithm; co-generation power plant; consumer load profile; multi-layered neural network; neural network approach; power generation automation; power generation scheduling; Artificial neural networks; Automation; Biological neural networks; Hybrid power systems; Neural networks; Neurons; Power generation; Power generation economics; Power system modeling; Scheduling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Energy Management and Power Delivery, 1998. Proceedings of EMPD '98. 1998 International Conference on
Print_ISBN :
0-7803-4495-2
Type :
conf
DOI :
10.1109/EMPD.1998.705543
Filename :
705543
Link To Document :
بازگشت