DocumentCode :
3298811
Title :
An Algorithm for Unit Commitment Based on Hopfield Neural Network
Author :
Gao, Weixin ; Tang, Nan ; Mu, Xiangyang
Author_Institution :
Shaanxi Key Lab. of Oil-Drilling Rigs Controlling Tech., Xi´´an Shiyou Univ., Xi´´an
Volume :
2
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
286
Lastpage :
290
Abstract :
This paper presents an algorithm, which is based on a Hopfield neural network, for determining unit commitment. By constructing an appropriate energy function, a single layer Hopfield neural network can solve the problem of assigning output power of generators at any given time. Based on this single layer Hopfield neural network, a multi-layer Hopfield neural network is presented. The multi-layer Hopfield neural network can solve the problem of power system unit commitment. The energy functions of single layer and multi-layer Hopfield neural network and the corresponding algorithm are given in the paper. The restricted conditions of the balance between power supply and demand, maximum and minimum outputs of power plants are considered in the energy function. So is the speed of propulsion and decreasing power of generators. An example shows that the result obtained by Hopfield neural network is somewhat similar to that obtained by genetic algorithm, but the calculation time is much shorter.
Keywords :
Hopfield neural nets; genetic algorithms; power distribution economics; power engineering computing; genetic algorithm; multilayer Hopfield neural network; power demand; power supply; power system unit commitment; unit commitment algorithm; Computer networks; Genetic algorithms; Hopfield neural networks; Laboratories; Mathematical model; Optimization methods; Particle swarm optimization; Power generation; Power system planning; Power systems; Hopfield neural network; optimization; planning; power system; unit commitment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.148
Filename :
4667002
Link To Document :
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