Extreme learning machine approach for sensorless wind speed estimation

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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