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

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Entropy 2017, 19(1), 6;
Received: 27 September 2016 / Revised: 17 November 2016 / Accepted: 30 November 2016 / Published: 1 January 2017
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. View Full-Text
Keywords: misalignment; DFWT; IEMD energy entropy; PSO; SVM misalignment; DFWT; IEMD energy entropy; PSO; SVM
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Xiao, Y.; Kang, N.; Hong, Y.; Zhang, G. Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM. Entropy 2017, 19, 6.

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