Wind turbine power coefficient estimation by soft computing methodologies: Comparative study

http://dx.doi.org/10.1016/j.enconman.2014.02.055 0196-8904/ 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel.: +6

[1]  Mohammed Chadli,et al.  Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach , 2012 .

[2]  Dario Rossi,et al.  Support vector regression for link load prediction , 2008 .

[3]  Qiang Ye,et al.  Sentiment classification of online reviews to travel destinations by supervised machine learning approaches , 2009, Expert Syst. Appl..

[4]  Dalibor Petkovic,et al.  Adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability , 2011, Neural Computing and Applications.

[5]  Sancho Salcedo-Sanz,et al.  Prediction of hourly O3 concentrations using support vector regression algorithms , 2010 .

[6]  Mahesh Pal,et al.  Support vector regression based modeling of pier scour using field data , 2011, Eng. Appl. Artif. Intell..

[7]  Jonathan M. Garibaldi,et al.  Supervised machine learning algorithms for protein structure classification , 2009, Comput. Biol. Chem..

[8]  L. S. Ng,et al.  Expert security system in wireless sensor networks based on fuzzy discussion multi-agent systems , 2010 .

[9]  M.R.H. Nobari,et al.  Prediction of optimum section pitch angle distribution along wind turbine blades , 2013 .

[10]  Andrew Kusiak,et al.  Dynamic control of wind turbines , 2010 .

[11]  Alexandra Balahur,et al.  Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis , 2014, Comput. Speech Lang..

[12]  Ronilson Rocha.  A sensorless control for a variable speed wind turbine operating at partial load , 2011 .

[13]  Mohd Yamani Idna Idris,et al.  Adaptive neuro-fuzzy estimation of optimal lens system parameters , 2014 .

[14]  Hui Li,et al.  Neural-network-based sensorless maximum wind energy capture with compensated power coefficient , 2004, IEEE Transactions on Industry Applications.

[15]  Ke Lu,et al.  An algorithm for semi-supervised learning in image retrieval , 2006, Pattern Recognition.

[16]  Enrico Zio,et al.  A dynamic particle filter-support vector regression method for reliability prediction , 2013, Reliab. Eng. Syst. Saf..

[17]  Qisheng Yan,et al.  Study on the Support Vector Regression Model for Order's Prediction , 2011 .

[18]  S. J. Kim,et al.  Modeling, control, and simulation of dual rotor wind turbine generator system , 2009 .

[19]  Ahmet Serdar Yilmaz,et al.  Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks , 2009, Expert Syst. Appl..

[20]  L. Ornella,et al.  Supervised machine learning and heterotic classification of maize ( Zea mays L.) using molecular marker data , 2010 .

[21]  Rohit Prasad,et al.  Batch-mode semi-supervised active learning for statistical machine translation , 2013, Comput. Speech Lang..

[22]  Paul Fleming,et al.  An Active Power Control System for Wind Turbines Capable of Primary and Secondary Frequency Control for Supporting Grid Reliability , 2013 .

[23]  Dalibor Petkovic,et al.  Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system , 2013, Expert Syst. Appl..

[24]  A. Boudghene Stambouli,et al.  A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system , 2011, Expert Syst. Appl..

[25]  Pei-Chann Chang,et al.  Iterated time series prediction with multiple support vector regression models , 2013, Neurocomputing.

[26]  Michael R. Lyu,et al.  Localized support vector regression for time series prediction , 2009, Neurocomputing.

[27]  Kjetil Uhlen,et al.  Smooth Transition Between Controllers for Floating Wind Turbines , 2012 .

[28]  Qingjin Meng,et al.  The Application of Fuzzy PID Control in Pitch Wind Turbine , 2012 .

[29]  Yüksel Oğuz,et al.  Adaptive neuro-fuzzy inference system to improve the power quality of variable-speed wind power generation system , 2010 .

[30]  Dalibor Petkovic,et al.  Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces , 2013, Expert Syst. Appl..

[31]  Yong Shi,et al.  Cost-Sensitive Support Vector Machine for Semi-Supervised Learning , 2013, ICCS.

[32]  Katharina Morik,et al.  Quality Prediction in Interlinked Manufacturing Processes based on Supervised & Unsupervised Machine Learning , 2013 .

[33]  Mohamed Cheriet,et al.  Help-Training for semi-supervised support vector machines , 2011, Pattern Recognit..

[34]  Mirna Issa,et al.  Intelligent rotational direction control of passive robotic joint with embedded sensors , 2013, Expert Syst. Appl..

[35]  Nor Badrul Anuar,et al.  An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique , 2013, Eng. Appl. Artif. Intell..

[36]  Huan Liu,et al.  Spectral feature selection for supervised and unsupervised learning , 2007, ICML '07.

[37]  Ainuddin Wahid Abdul Wahab,et al.  Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission , 2014 .

[38]  Dalibor Petković,et al.  Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation , 2013 .

[39]  Mohamed Mohandes,et al.  Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS) , 2011 .

[40]  T.-L. Lee,et al.  Support vector regression methodology for storm surge predictions , 2008 .

[41]  Luis M. Fernández,et al.  Equivalent models of wind farms by using aggregated wind turbines and equivalent winds , 2009 .

[42]  Yücel Koçyigit,et al.  An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines , 2010, Expert Syst. Appl..

[43]  Ling Zhuang,et al.  Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization , 2009, Expert Syst. Appl..

[44]  Takashi Hiyama,et al.  Optimal control of variable-speed wind energy conversion system based on fuzzy model power curve , 2012 .

[45]  Daoliang Li,et al.  A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction , 2013, Math. Comput. Model..

[46]  Youyi Wang,et al.  Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system , 2013, Neurocomputing.

[47]  Hui Jiang,et al.  Grey relational grade in local support vector regression for financial time series prediction , 2012, Expert Syst. Appl..

[48]  Mao-Hsiung Chiang,et al.  A novel pitch control system for a wind turbine driven by a variable-speed pump-controlled hydraulic servo system , 2011 .

[49]  Yan Leng,et al.  Combining active learning and semi-supervised learning to construct SVM classifier , 2013, Knowl. Based Syst..

[50]  Jan-Ming Ho,et al.  Travel time prediction with support vector regression , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[51]  Kyoung-Soo Ro,et al.  Application of neural network controller for maximum power extraction of a grid-connected wind turbine system , 2005 .

[52]  M. Sedighizadeh,et al.  Adaptive PID control of wind energy conversion systems using RASP1 mother wavelet basis function networks , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[53]  S. Chakraborty.  Bayesian semi-supervised learning with support vector machine , 2011 .

[54]  D. Basak,et al.  Support Vector Regression , 2008 .

[55]  Junji Tamura,et al.  Variable speed wind turbine generator system with current controlled voltage source inverter , 2011 .