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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
Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Algorithms 2017, 10(2), 67;
Received: 2 May 2017 / Revised: 7 June 2017 / Accepted: 8 June 2017 / Published: 10 June 2017
PDF [1367 KB, uploaded 13 June 2017]


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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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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