Fault Diagnosis of Wind Turbine Double-Fed Induction Generator Based on Multi-Level Fusion and Measurement of Back-to-Back Converter Current Signal

One of the major faults in Doubly-Fed Induction Generator (DFIG) is the Inter-Turn Short Circuit (ITSC) fault. This fault leads to an asymmetry between phases and causes problems to the normal state between current lines. Faults diagnosis from non-stationary signals for the Wind Turbine (WT) is difficult. Therefore, the strategy of fault diagnosis must be robust against instability. In this paper, a new intelligent strategy based on multi-level fusion is proposed for diagnosis of DFIG inter-turn stator winding fault. Firstly, to overcome the non-stationary nature of the vibration signals of the WT, empirical mode decomposition (EMD) method is performed in time-frequency domains to extract best fault features from information power sensor and information current sensor. Moreover, a feature evaluation technique is used for the input of the classifier to choose the best subset features. Secondly, Least Squares Wavelet Support Vector Machines (LS-WSVM) classifier is trained to classify fault types based on feature level fusion (FLF) from different sensors. The main parameters of SVM and the kernel function are optimized by Genetic Algorithm (GA). Finally, Dempster-Shafer evidential reasoning (DSER) is used for fusing the GA-LS-WSVM results based on decision level fusion (DLF) of individual classifiers. In order to evaluate the proposed strategy, a DFIG WT test rig is developed. The experimental results show the efficiency of the proposed structure compared to other ITSC fault diagnosis methods. The results show that the classification accuracy of DSER-GA-LS-WSVM is 98.27%.