Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM

Misalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, Solidworks and Adams are used to simulate the different operating conditions of the transmission system of the DFWT to obtain the corresponding characteristic signals. Improved empirical mode decomposition (IEMD), which improves the end effects of empirical mode decomposition (EMD) is used to decompose the signals to get intrinsic mode function (IMF), and the IEMD energy entropy reflecting the working state are extracted as the inputs of the support vector machine (SVM). Particle swarm optimization (PSO) is used to optimize the parameters of SVM to improve the classification performance. The results show that the proposed method can effectively and accurately identify the types of misalignment of the DFWT.

[1]  Moamar Sayed Mouchaweh,et al.  Hybrid dynamic classifier for drift-like fault diagnosis in a class of hybrid dynamic systems: Application to wind turbine converters , 2016, Neurocomputing.

[2]  Damiano Rotondo,et al.  FDI and FTC of wind turbines using the interval observer approach and virtual actuators/sensors , 2014 .

[3]  Damiano Rotondo,et al.  An Interval NLPV Parity Equations Approach for Fault Detection and Isolation of a Wind Farm , 2015, IEEE Transactions on Industrial Electronics.

[4]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[5]  Mingxia He,et al.  A simple boundary process technique for empirical mode decomposition , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[6]  Azriel Rosenfeld,et al.  Pattern recognition: Historical perspective and future directions , 2000 .

[7]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[8]  Birgitte Bak-Jensen,et al.  Coordination between Fault-Ride-Through Capability and Over-current Protection of DFIG Generators for Wind Farms , 2010 .

[9]  Yan Li,et al.  Wigner-Ville distribution , 2008 .

[10]  Jun Tang,et al.  Research on Fault Diagnosis Based on SVM , 2013 .

[11]  Radu-Emil Precup,et al.  An overview on fault diagnosis and nature-inspired optimal control of industrial process applications , 2015, Comput. Ind..

[12]  Ding Shu.  Study on wind turbine gearbox fault diagnosis based on LVQ neural network , 2014 .

[13]  Wang Si-ming.  Fault diagnosis of wind turbine gearbox based on the wavelet decomposition and least square support vector machine , 2011 .

[14]  Vicenç Puig,et al.  Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach , 2015, Annu. Rev. Control..

[15]  Chunheng Wang,et al.  Sparse Representation Based on K-Nearest Neighbor Classifier for Degraded Chinese Character Recognition , 2010, PCM.