• 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