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Algorithms 2017, 10(2), 67; doi:10.3390/a10020067

Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
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Academic Editor: Javier Del Ser Lorente
Received: 2 May 2017 / Revised: 7 June 2017 / Accepted: 8 June 2017 / Published: 10 June 2017
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Abstract

The misalignment of the drive system of the DFIG (Doubly Fed Induction Generator) wind turbine is one of the important factors that cause damage to the gears, bearings of the high-speed gearbox and the generator bearings. How to use the limited information to accurately determine the type of failure has become a difficult study for the scholars. In this paper, the time-domain indexes and frequency-domain indexes are extracted by using the vibration signals of various misaligned simulation conditions of the wind turbine drive system, and the time-frequency domain features—energy entropy are also extracted by the IEMD (Improved Empirical Mode Decomposition). A mixed-domain feature set is constructed by them. Then, SVM (Support Vector Machine) is used as the classifier, the mixed-domain features are used as the inputs of SVM, and PSO (Particle Swarm Optimization) is used to optimize the parameters of SVM. The fault types of misalignment are classified successfully. Compared with other methods, the accuracy of the given fault isolation model is improved. View Full-Text
Keywords: misalignment; fault isolation; mixed-domain features; SVM; PSO misalignment; fault isolation; mixed-domain features; SVM; PSO
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xiao, Y.; Wang, Y.; Mu, H.; Kang, N. Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features. Algorithms 2017, 10, 67.

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