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.
Highlights
With the rapid development of wind power, a lot of wind turbines have faults in the operation because most of them are installed in remote areas, and their loads are unstable
The results show that the proposed method can identify the types of misalignment effectively compared with other methods
EMD is improved by the method of mirror extension, and the energy entropy of the vibration signal is extracted by the improved EMD (IEMD)
Summary
With the rapid development of wind power, a lot of wind turbines have faults in the operation because most of them are installed in remote areas, and their loads are unstable. [14], the vibration signals of three typical states of normal conditions, tooth wear and tooth breakage of the gearbox in the wind turbine are analyzed, and the margin index, kurtosis index, peak index, pulse index, power spectrum entropy in the frequency-domain were extracted, the time-domain and frequency-domain features were the inputs in the fault isolation. In order to extract the feature parameters which reflect the vibration signal as much as possible, and to make the fault isolation more reliable and accurate, in this paper, firstly, the time-domain, frequency-domain and time-frequency domain of the wind turbine vibration signal are combined to construct the mixed-domain feature library to obtain more comprehensive and accurate fault isolation information. The results show that the proposed method can identify the types of misalignment effectively compared with other methods
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