Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated.

[1]  J. Noh,et al.  The Effects of Ocean Acidification and Warming on Growth of a Natural Community of Coastal Phytoplankton , 2020 .

[2]  Yufei Tang,et al.  Rotor blade imbalance fault detection for variable-speed marine current turbines via generator power signal analysis , 2021 .

[3]  Xin Ma,et al.  Study of an Electromagnetic Ocean Wave Energy Harvester Driven by an Efficient Swing Body Toward the Self-Powered Ocean Buoy Application , 2019, IEEE Access.

[4]  Mohamed Benbouzid,et al.  An imbalance fault detection method based on data normalization and EMD for marine current turbines. , 2017, ISA transactions.

[5]  Tianzhen Wang,et al.  An Imbalance Fault Detection Method for MCTs Using Voltage Signal , 2019, 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS).

[6]  E. Mendoza,et al.  A Review on Environmental and Social Impacts of Thermal Gradient and Tidal Currents Energy Conversion and Application to the Case of Chiapas, Mexico , 2020, International journal of environmental research and public health.

[7]  Javad Poshtan,et al.  Fault Diagnosis of Brushless DC Motors Using Built-In Hall Sensors , 2019, IEEE Sensors Journal.

[8]  Yuan-Hai Shao,et al.  Robust and Sparse Linear Discriminant Analysis via an Alternating Direction Method of Multipliers , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Roger Ivor Grosvenor,et al.  An approach to the characterisation of the performance of a tidal stream turbine , 2017 .

[10]  Mohamed Benbouzid,et al.  VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets , 2020 .

[11]  Elhoussin Elbouchikhi,et al.  Induction Machines Fault Detection Based on Subspace Spectral Estimation , 2016, IEEE Transactions on Industrial Electronics.

[12]  Tao Xie,et al.  Imbalance Fault Detection Based on the Integrated Analysis Strategy for Marine Current Turbines under Variable Current Speed , 2020, Entropy.

[13]  Tao Xie,et al.  A review of current issues of marine current turbine blade fault detection , 2020 .

[14]  Ahmad Forouzantabar,et al.  Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS , 2019, IET Electric Power Applications.

[15]  Tianhao Tang,et al.  Imbalance fault detection of marine current turbine under condition of wave and turbulence , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[16]  Xiaoqiang Zhao,et al.  A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing , 2020, Applied Intelligence.

[17]  Prasanta Kundu,et al.  A Novel Approach for Sensitive Inter-turn Fault Detection in Induction Motor Under Various Operating Conditions , 2019, Arabian Journal for Science and Engineering.

[18]  Reza Mohseni,et al.  Gradient Ascent Optimization for Fault Detection in Electrical Power Systems based on Wavelet Transformation , 2019 .

[19]  Mohamed Benbouzid,et al.  A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines , 2020, IEEE Access.

[20]  Arturo Garcia-Perez,et al.  Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection , 2019, IEEE Transactions on Industrial Electronics.

[21]  Mohamed Benbouzid,et al.  Frequency and Phasor Estimations in Three-Phase Systems: Maximum Likelihood Algorithms and Theoretical Performance , 2019, IEEE Transactions on Smart Grid.

[22]  Paul Mycek,et al.  Experimental study of the turbulence intensity effects on marine current turbines behaviour. Part I: One single turbine , 2014 .

[23]  Elias G. Strangas,et al.  On the Accuracy of Fault Detection and Separation in Permanent Magnet Synchronous Machines Using MCSA/MVSA and LDA , 2016, IEEE Transactions on Energy Conversion.

[24]  Yide Wang,et al.  A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine , 2018, Sensors.

[25]  Kehui Xu,et al.  Tidal and Storm Impacts on Hydrodynamics and Sediment Dynamics in an Energetic Ebb Tidal Delta , 2020 .

[26]  Guolong Cui,et al.  Beampattern Synthesis With Sidelobe Control and Applications , 2020, IEEE Transactions on Antennas and Propagation.

[27]  Poonam Singal,et al.  Networks of Underwater Sensor Wireless Systems: Latest Problems and Threats , 2021, Int. J. Wirel. Networks Broadband Technol..

[28]  Barry W. Williams,et al.  Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review , 2018 .

[29]  T. Simas,et al.  Marine Biofouling: A European Database for the Marine Renewable Energy Sector , 2020, Journal of Marine Science and Engineering.

[30]  Mohamed Benbouzid,et al.  Developments in large marine current turbine technologies – A review , 2017 .

[31]  Fernando Borges,et al.  An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection , 2020, IEEE Latin America Transactions.

[32]  Mohamed Machmoum,et al.  Attraction, Challenge and Current Status of Marine Current Energy , 2018, IEEE Access.

[33]  Dong Wang,et al.  Rub-Impact Fault Diagnosis of Rotating Machinery Based on 1-D Convolutional Neural Networks , 2020, IEEE Sensors Journal.

[34]  James H. VanZwieten,et al.  Rotor blade imbalance fault detection for variable-speed marine current turbines via generator power signal analysis , 2020, Ocean Engineering.

[35]  Shahin Hedayati Kia,et al.  Stray Flux Monitoring for Reliable Detection of Rotor Faults Under the Influence of Rotor Axial Air Ducts , 2019, IEEE Transactions on Industrial Electronics.

[36]  Dianguo Xu,et al.  Motor Speed Signature Analysis for Local Bearing Fault Detection With Noise Cancellation Based on Improved Drive Algorithm , 2020, IEEE Transactions on Industrial Electronics.