Extreme learning machine approach for sensorless wind speed estimation |
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Affiliation: | 1. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Institute of Ocean and Earth Sciences (IOES), University of Malaya, 50603 Kuala Lumpur, Malaysia;3. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;4. University of Ni?, Faculty of Mechanical Engineering, Department for Mechatronics and Control, Aleksandra Medvedeva 14, 18000 Ni?, Serbia;5. Department of Civil and Environmental Engineering, ITM University, Gurgaon, Haryana, India;1. Department of Mechanical and Process Engineering, ETH Zurich, Switzerland;2. Model Predictive Control Laboratory, University of California Berkeley, USA;3. ABB Switzerland Ltd., Corporate Research, Baden-Daettwil, Switzerland;4. Automatic Control Laboratory, ETH Zurich, Switzerland;1. School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD, Scotland, UK;2. Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne, UK;1. School of Statistics, Xi ‘An University of Finance and Economics, No.360, Changning Street, Chang ‘An District, Xian, Shaanxi Province, 710100, China;2. School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Road, Shahekou District, Dalian, Liaoning Province, 116025, China |
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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 analyzed 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 utilized 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. |
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Keywords: | Wind speed Soft computing Extreme learning machine Estimation Sensorless |
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