• DocumentCode
    2671926
  • Title

    An Introduction to the Echo State Network and its Applications in Power System

  • Author

    Dai, Jing ; Venayagamoorthy, Ganesh K. ; Harley, Ronald G.

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
  • fYear
    2009
  • fDate
    8-12 Nov. 2009
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    Echo state network (ESN) is a new type of recurrent neural network (RNN) proposed in recent years. The training process of ESN is easier and requires less computational effort than regular RNN which has the same size. Due to its high modeling capability of complex dynamic system, ESN has been used in various power system applications such as power system nonlinear load modeling and true harmonic current detection, wide area monitoring, intelligent control of an active power filter (APF), overhead conductor thermal dynamics identification, wind speed or water inflow forecasting, etc. This paper introduces the basic concept and the offline and online training algorithms of the ESN in detail and reviews the state of the art of ESN applications in power systems.
  • Keywords
    learning (artificial intelligence); power system harmonics; power system measurement; power system simulation; recurrent neural nets; active power filter; echo state network; overhead conductor thermal dynamics identification; power system nonlinear load modeling; recurrent neural network; true harmonic current detection; wide area monitoring; Active filters; Nonlinear dynamical systems; Power harmonic filters; Power system control; Power system dynamics; Power system harmonics; Power system modeling; Power systems; Predictive models; Recurrent neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent System Applications to Power Systems, 2009. ISAP '09. 15th International Conference on
  • Conference_Location
    Curitiba
  • Print_ISBN
    978-1-4244-5097-8
  • Type

    conf

  • DOI
    10.1109/ISAP.2009.5352913
  • Filename
    5352913