Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production
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Mehrdad Saif | Mojtaba Kordestani | Rupp Carriveau | David S.-K. Ting | Majid Morshedizadeh | D. Ting | M. Saif | R. Carriveau | Majid Morshedizadeh | Mojtaba Kordestani
[1] Sofiane Achiche,et al. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description , 2013, Appl. Soft Comput..
[2] Tariq Samad,et al. Self–organization with partial data , 1992 .
[3] Wei Liang,et al. An integrated safety prognosis model for complex system based on dynamic Bayesian network and ant colony algorithm , 2011, Expert Syst. Appl..
[4] Andrew Kusiak,et al. Models for monitoring wind farm power , 2009 .
[5] Jing Pan,et al. Numerical Forecasting Experiment of the Wave Energy Resource in the China Sea , 2016 .
[6] Wenbin Wang. A two-stage prognosis model in condition based maintenance , 2007, Eur. J. Oper. Res..
[7] Andrew Kusiak,et al. On-line monitoring of power curves , 2009 .
[8] J. Graham,et al. Missing data analysis: making it work in the real world. , 2009, Annual review of psychology.
[9] Zhongzhi Shi,et al. Extended rough set-based attribute reduction in inconsistent incomplete decision systems , 2012, Inf. Sci..
[10] John W. Sheppard,et al. A Bayesian approach to diagnosis and prognosis using built-in test , 2005, IEEE Transactions on Instrumentation and Measurement.
[11] Wenxian Yang,et al. Wind turbine condition monitoring and reliability analysis by SCADA information , 2011, 2011 Second International Conference on Mechanic Automation and Control Engineering.
[12] Selin Aviyente,et al. Discovering the hidden health states in bearing vibration signals for fault prognosis , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.
[13] David He,et al. Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis , 2007, Eur. J. Oper. Res..
[14] Isao Hayashi,et al. NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..
[15] Mitra Fouladirad,et al. A methodology for probabilistic model-based prognosis , 2013, Eur. J. Oper. Res..
[16] A. Kusiak,et al. Modeling wind-turbine power curve: A data partitioning and mining approach , 2017 .
[17] Pietro Vecchio,et al. Wind energy prediction using a two-hidden layer neural network , 2010 .
[18] H. Khalil,et al. Wavelet-based methods for the prognosis of mechanical and electrical failures in electric motors , 2005 .
[19] Takao Maeda,et al. Effect of solidity on aerodynamic forces around straight-bladed vertical axis wind turbine by wind tunnel experiments (depending on number of blades) , 2016 .
[20] Kevin Swingler,et al. Applying neural networks - a practical guide , 1996 .
[21] Man Gyun Na,et al. APPLICATION OF MONITORING, DIAGNOSIS, AND PROGNOSIS IN THERMAL PERFORMANCE ANALYSIS FOR NUCLEAR POWER PLANTS , 2014 .
[22] Dalibor Petković,et al. Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation , 2013 .
[23] Meik Schlechtingen,et al. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection , 2011 .
[24] Wenyuan Lv,et al. A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis , 2015 .
[25] Sunday O. Olatunji,et al. Investigating the effect of correlation-based feature selection on the performance of support vector machines in reservoir characterization , 2015 .
[26] Ao Li,et al. Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme , 2006, BMC Bioinformatics.
[27] M. A. Djeziri,et al. Data driven and model based fault prognosis applied to a mechatronic system , 2013, 4th International Conference on Power Engineering, Energy and Electrical Drives.
[28] Ahmet Arslan,et al. A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm , 2013, Inf. Sci..
[29] Siegfried Heier,et al. Grid Integration of Wind Energy Conversion Systems , 1998 .
[30] Hui Guo,et al. Numerical simulation and experimental validation of ultrasonic de-icing system for wind turbine blade , 2016 .
[31] Asifullah Khan,et al. Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks , 2017 .
[32] Lukasz A. Kurgan,et al. Impact of imputation of missing values on classification error for discrete data , 2008, Pattern Recognit..
[33] Robert X. Gao,et al. Prognosis of Defect Propagation Based on Recurrent Neural Networks , 2011, IEEE Transactions on Instrumentation and Measurement.
[34] Kyung Chun Kim,et al. CFD study on aerodynamic power output of a 110 kW building augmented wind turbine , 2016 .
[35] Mimoun Zelmat,et al. DESIGN OF A FUZZY MODEL-BASED CONTROLLER FOR A DRUM BOILER-TURBINE SYSTEM , 2004 .
[36] Aníbal R. Figueiras-Vidal,et al. Pattern classification with missing data: a review , 2010, Neural Computing and Applications.
[37] Quan Wang,et al. A new direct design method of wind turbine airfoils and wind tunnel experiment , 2016 .
[38] Gang Niu,et al. Dempster–Shafer regression for multi-step-ahead time-series prediction towards data-driven machinery prognosis , 2009 .
[39] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[40] Shuhui Li,et al. Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation , 2001 .
[41] Shiyu Zhou,et al. Evaluation and Comparison of Mixed Effects Model Based Prognosis for Hard Failure , 2013, IEEE Transactions on Reliability.
[42] Karim Salahshoor,et al. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers , 2010 .
[43] Asifullah Khan,et al. Wind power prediction using deep neural network based meta regression and transfer learning , 2017, Appl. Soft Comput..
[44] Lei Huang,et al. Prognosis of Hybrid Systems With Multiple Incipient Faults: Augmented Global Analytical Redundancy Relations Approach , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[45] Claudomiro Sales,et al. Multi-objective genetic algorithm for missing data imputation , 2015, Pattern Recognit. Lett..
[46] Gang Li,et al. Reconstruction based fault prognosis for continuous processes , 2010 .
[47] Qinghua Hu,et al. Transfer learning for short-term wind speed prediction with deep neural networks , 2016 .
[48] Arun Ross,et al. A comparison of imputation methods for handling missing scores in biometric fusion , 2012, Pattern Recognit..
[49] Venkadesh Raman,et al. Numerical simulation analysis as a tool to identify areas of weakness in a turbine wind-blade and solutions for their reinforcement , 2016 .
[50] Peter K. Sharpe,et al. Dealing with missing values in neural network-based diagnostic systems , 1995, Neural Computing & Applications.
[51] Wenbin Wang,et al. An off-online fuzzy modelling method for fault prognosis with an application , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).
[52] Zili Zhang,et al. Missing Value Estimation for Mixed-Attribute Data Sets , 2011, IEEE Transactions on Knowledge and Data Engineering.
[53] E. H. Mamdani,et al. Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.
[54] Joseph Mathew,et al. Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .
[55] Leila Naderloo,et al. Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) , 2016 .
[56] Bin Zhang,et al. An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems , 2012, Expert Syst. Appl..
[57] Jian Hou,et al. Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes , 2016, Neurocomputing.
[58] Mehrdad Saif,et al. Improved power curve monitoring of wind turbines , 2017 .
[59] Md Zahidul Islam,et al. Missing value imputation using decision trees and decision forests by splitting and merging records: Two novel techniques , 2013, Knowl. Based Syst..
[60] Joseph Mathew,et al. Bearing fault prognosis based on health state probability estimation , 2012, Expert Syst. Appl..
[61] Yaojie Lu,et al. Generalized expectation–maximization approach to LPV process identification with randomly missing output data , 2015 .
[62] Michio Sugeno,et al. Industrial Applications of Fuzzy Control , 1985 .
[63] Yi Wang,et al. Prognosis of Underground Cable via Online Data-Driven Method With Field Data , 2015, IEEE Transactions on Industrial Electronics.
[64] Lin Lin Xia,et al. An overview of global ocean wind energy resource evaluations , 2016 .
[65] Ashwani Kumar,et al. Free Vibration Analysis of Al 2024 Wind Turbine Blade Designed for Uttarakhand Region based on FEA , 2014 .
[66] Shichao Zhang,et al. Shell-neighbor method and its application in missing data imputation , 2011, Applied Intelligence.
[67] Sung-Ho Kim,et al. Design of wind turbine fault detection system based on performance curve , 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.
[68] Aly M. Elzahaby,et al. CFD analysis of flow fields for shrouded wind turbine’s diffuser model with different flange angles , 2017 .
[69] Gene H. Golub,et al. Missing value estimation for DNA microarray gene expression data: local least squares imputation , 2005, Bioinform..
[70] Witold Pedrycz,et al. A Novel Framework for Imputation of Missing Values in Databases , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[71] M. Schlechtingen,et al. Using Data-Mining Approaches for Wind Turbine Power Curve Monitoring: A Comparative Study , 2013, IEEE Transactions on Sustainable Energy.