• DocumentCode
    3221074
  • Title

    Power system controller design using multi-population PBIL

  • Author

    Folly, K.A. ; Venayagamoorthy, Ganesh K.

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Cape Town, Cape Town, South Africa
  • fYear
    2013
  • fDate
    16-19 April 2013
  • Firstpage
    37
  • Lastpage
    43
  • Abstract
    The application of a multi-population based Population-Based Incremental Learning (PBIL) to power system controller design is presented in this paper. PBIL is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. Single population PBIL has recently received increasing attention in various engineering fields due to its effectiveness., easy implementation and robustness. Despite these strengths., PBIL still suffers from issues of loss of diversity in the population. The use of multi-population is seen as one way of increasing the diversity in the population. The approach is applied to power system controller design. Simulations results show that the multi-population PBIL approach performs better than the standard PBIL and is as effective as PBIL where adaptive learning is used.
  • Keywords
    evolutionary computation; learning (artificial intelligence); neural nets; power system control; artificial neural networks; competitive learning; evolutionary optimization; multipopulation PBIL; population based incremental learning; power system controller design; Control systems; Optimization; Power system stability; Sociology; Standards; Statistics; Vectors; Adaptive learning rate; PBIL; low-frequency oscillations; multi-population; power system stabilizer;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence Applications In Smart Grid (CIASG), 2013 IEEE Symposium on
  • Conference_Location
    Singapore
  • ISSN
    2326-7682
  • Type

    conf

  • DOI
    10.1109/CIASG.2013.6611496
  • Filename
    6611496