• DocumentCode
    173626
  • Title

    Continuous, non-linear, optimal speed control of a Distributed Generation Power Pack using Artificial Neural Networks

  • Author

    Hill, Christopher Ian ; Zanchetta, Pericle ; Okaeme, Nnamdi A. ; Bozhko, Serhiy V.

  • Author_Institution
    Power Electron., Machines & Control Res. Group, Univ. of Nottingham, Nottingham, UK
  • fYear
    2014
  • fDate
    13-16 May 2014
  • Firstpage
    1050
  • Lastpage
    1055
  • Abstract
    Distributed Generation Power Packs with a combustion engine prime mover are still widely used to supply electric power in a variety of applications. These applications range from backup power supply systems to providing power in places where grid connection is either technically impractical or financially uneconomic. Due to the ever increasing cost of diesel fuel and the environmental issues associated with its use, the optimisation of these AC generators and the reduction of fuel consumption is vital. This paper presents how Artificial Neural Networks can be utilised in order to obtain a continuous function which relates variable load demand to optimal speed demand. The Artificial Neural Network toolbox within MATLAB is used to create, train and test the Artificial Neural Networks. This paper also shows the results of an experimental system used in order to emulate the Distributed Generation Power Pack. Overall it is shown that is possible to operate a variable speed system under optimal, non-linear, speed control using Artificial Neural Networks.
  • Keywords
    continuous systems; control engineering computing; distributed power generation; neural nets; nonlinear control systems; optimal control; optimisation; power engineering computing; power supplies to apparatus; power system stability; AC generator optimisation; MATLAB; artificial neural network; backup power supply system; combustion engine prime mover; continuous function; diesel fuel cost; distributed generation power pack; environmental issues; grid connection; nonlinear speed control; optimal speed control; optimal speed demand; variable load demand; Artificial neural networks; DC motors; Distributed power generation; Fuels; MATLAB; Optimization; Training; ANN; Distributed Generation; Optimal Control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Energy Conference (ENERGYCON), 2014 IEEE International
  • Conference_Location
    Cavtat
  • Type

    conf

  • DOI
    10.1109/ENERGYCON.2014.6850554
  • Filename
    6850554