Automatic identification of wind turbine models using evolutionary multiobjective optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "William {La Cava} and Kourosh Danai and 
                 Lee Spector and Paul Fleming and Alan Wright and Matthew Lackner",
  title =        "Automatic identification of wind turbine models using
                 evolutionary multiobjective optimization",
  journal =      "Renewable Energy",
  volume =       "87, Part 2",
  pages =        "892--902",
  year =         "2016",
  note =         "Optimization Methods in Renewable Energy Systems
  ISSN =         "0960-1481",
  DOI =          "doi:10.1016/j.renene.2015.09.068",
  URL =          "",
  abstract =     "Modern industrial-scale wind turbines are nonlinear
                 systems that operate in turbulent environments. As
                 such, it is difficult to characterize their behaviour
                 accurately across a wide range of operating conditions
                 using physically meaningful models. Customarily, the
                 models derived from wind turbine data are in `black
                 box' format, lacking in both conciseness and
                 intelligibility. To address these deficiencies, we use
                 a recently developed symbolic regression method to
                 identify models of a modern horizontal-axis wind
                 turbine in symbolic form. The method uses evolutionary
                 multiobjective optimization to produce succinct dynamic
                 models from operational data while making minimal
                 assumptions about the physical properties of the
                 system. We compare the models produced by this method
                 to models derived by other methods according to their
                 estimation capacity and evaluate the trade-off between
                 model intelligibility and accuracy. Several succinct
                 models are found that predict wind turbine behaviour as
                 well as or better than more complex alternatives
                 derived by other methods. We interpret the new models
                 to show that they often contain intelligible estimates
                 of real process physics.",
  keywords =     "genetic algorithms, genetic programming, Wind energy,
                 System identification, Multiobjective optimization",

Genetic Programming entries for William La Cava Kourosh Danai Lee Spector Paul Fleming Alan Wright Matthew A Lackner