Extreme learning machine approach for sensorless wind speed estimation

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@Article{Nikolic:2016:Mechatronics,
  author =       "Vlastimir Nikolic and Shervin Motamedi and 
                 Shahaboddin Shamshirband and Dalibor Petkovic and Sudheer Ch and 
                 Mohammad Arif",
  title =        "Extreme learning machine approach for sensorless wind
                 speed estimation",
  journal =      "Mechatronics",
  volume =       "34",
  pages =        "78--83",
  year =         "2016",
  note =         "System-Integrated Intelligence: New Challenges for
                 Product and Production Engineering",
  ISSN =         "0957-4158",
  DOI =          "doi:10.1016/j.mechatronics.2015.04.007",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957415815000525",
  abstract =     "Precise predictions of wind speed play important role
                 in determining the feasibility of harnessing wind
                 energy. In fact, reliable wind predictions offer secure
                 and minimal economic risk situation to operators and
                 investors. This paper presents a new model based upon
                 extreme learning machine (ELM) for sensor-less
                 estimation of wind speed based on wind turbine
                 parameters. The inputs for estimating the wind speed
                 are wind turbine power coefficient, blade pitch angle,
                 and rotational speed. In order to validate authors
                 compared prediction of ELM model with the predictions
                 with genetic programming (GP), artificial neural
                 network (ANN) and support vector machine with radial
                 basis kernel function (SVM-RBF). This investigation
                 analysed the reliability of these computational models
                 using the simulation results and three statistical
                 tests. The three statistical tests includes the Pearson
                 correlation coefficient, coefficient of determination
                 and root-mean-square error. Finally, this study
                 compared predicted wind speeds from each method against
                 actual measurement data. Simulation results, clearly
                 demonstrate that ELM can be used effectively in
                 applications of sensor-less wind speed predictions.
                 Concisely, the survey results show that the proposed
                 ELM model is suitable and precise for sensor-less wind
                 speed predictions and has much higher performance than
                 the other approaches examined in this study.",
  keywords =     "genetic algorithms, genetic programming, Wind speed,
                 Soft computing, Extreme learning machine, Estimation,
                 Sensor less",
  notes =        "University of Nis, Faculty of Mechanical Engineering,
                 Department for Mechatronics and Control, Aleksandra
                 Medvedeva 14, 18000 Nis, Serbia",
}

Genetic Programming entries for Vlastimir Nikolic Shervin Motamedi Shahaboddin Shamshirband Dalibor Petkovic Sudheer Ch Mohammad Arif

Citations