Research on the damage prediction method of offshore wind turbine tower structure based on improved neural network

Abstract Tower structure is a basic part of offshore wind power system, and how to ensure the stability and safety of tower structure in wind turbine operation and develop the efficient and accurate damage prediction method to judge the damage location and degree of tower structure has become an important research topic in the offshore wind power industry. With high adaptability, nonlinearity and strong function approximation ability, Artificial neural network (ANN) has been widely used in structural damage prediction and showed better performance than other diagnostic methods, however, traditional Back Propagation neural network (BPNN) has shortcomings of complex training parameters and low accuracy. To address this issue, this paper considered an improved neural network damage prediction method based on step-by-step identification for offshore wind turbine tower structure, an experimental research was carried out to simplify wind turbine model and experimentally measure the intact steel cylinders with different damage locations and different damage degrees. Then, the modal analysis was carried out by using the numerical analysis method and the results were compared with those of experimental measurement to modify the model. The training data are generated by Abaqus software to verify the proposed method. Results showed that the step-by-step prediction method could effectively reduce the complexity of the network, improve the prediction accuracy of the neural network, and save the training time.

[1]  David McMillan,et al.  Availability, operation and maintenance costs of offshore wind turbines with different drive train configurations , 2017 .

[2]  Zubaidah Ismail,et al.  A comparative study of vibrational response based impact force localization and quantification using radial basis function network and multilayer perceptron , 2017, Expert Syst. Appl..

[3]  Zaccheus Olaofe Olaofe.  Review of energy systems deployment and development of offshore wind energy resource map at the coastal regions of Africa , 2018, Energy.

[4]  Ajit R Godbole,et al.  Effects of aerodynamic damping on the tower load of offshore horizontal axis wind turbines , 2017 .

[5]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[6]  W. Y. Liu,et al.  The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review , 2015 .

[7]  Sung-Ryul Kim,et al.  Effect of monopile foundation modeling on the structural response of a 5-MW offshore wind turbine tower , 2015 .

[8]  Umberto Meneghetti,et al.  Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings , 2001 .

[9]  Yue Zhao,et al.  Structural vibration monitoring and operational modal analysis of offshore wind turbine structure , 2018 .

[10]  Sanjay R. Arwade,et al.  Hurricane risk assessment of offshore wind turbines , 2018, Renewable Energy.

[11]  Sergio Martín-Martínez,et al.  Wind turbine reliability: A comprehensive review towards effective condition monitoring development , 2018, Applied Energy.

[12]  M Feyzollahzadeh,et al.  Wind load response of offshore wind turbine towers with fixed monopile platform , 2016 .

[13]  Robert D. Adams,et al.  The location of defects in structures from measurements of natural frequencies , 1979 .

[14]  Reza Rooki,et al.  Application of general regression neural network (GRNN) for indirect measuring pressure loss of Herschel–Bulkley drilling fluids in oil drilling , 2016 .

[15]  Jung-Ryul Lee,et al.  Structural health monitoring for a wind turbine system: a review of damage detection methods , 2008 .

[16]  P. D. McFadden,et al.  APPLICATION OF WAVELETS TO GEARBOX VIBRATION SIGNALS FOR FAULT DETECTION , 1996 .

[17]  Siamak Noroozi,et al.  Artificial neural networks for vibration based inverse parametric identifications: A review , 2017, Appl. Soft Comput..

[18]  Lionel Tarassenko,et al.  Static and dynamic novelty detection methods for jet engine health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[19]  Hoon Sohn,et al.  Damage diagnosis under environmental and operational variations using unsupervised support vector machine , 2009 .

[20]  Alhussein Albarbar,et al.  Wind turbine blades condition assessment based on vibration measurements and the level of an empirically decomposed feature , 2012 .

[21]  D. Osypiw,et al.  On-Line Vibration Analysis with Fast Continuous Wavelet Algorithm for Condition Monitoring of Bearing , 2003 .

[22]  Alireza Rahai,et al.  An integrated ANN-GA for reliability based inspection of concrete bridge decks considering extent of corrosion-induced cracks and life cycle costs , 2012 .

[23]  Jan Becker Høgsberg,et al.  Evaluation of damping estimates by automated Operational Modal Analysis for offshore wind turbine tower vibrations , 2018 .

[24]  João Cardoso,et al.  Review and application of Artificial Neural Networks models in reliability analysis of steel structures , 2015 .

[25]  P. Tse,et al.  Singularity analysis of the vibration signals by means of wavelet modulus maximal method , 2007 .

[26]  Keith Worden,et al.  Damage identification using support vector machines , 2001 .

[27]  Maurizio Collu,et al.  On intermediate-scale open-sea experiments on floating offshore structures: Feasibility and application on a spar support for offshore wind turbines , 2018, Marine Structures.

[28]  Zhipeng Feng,et al.  Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation , 2012 .

[29]  Fei Yan,et al.  New strategy for anchorage reliability assessment of GFRP bars to concrete using hybrid artificial neural network with genetic algorithm , 2016 .

[30]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[31]  Sung-Hoon Ahn,et al.  Condition monitoring and fault detection of wind turbines and related algorithms: A review , 2009 .