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Fault Diagnosis of Wind Turbine Double-Fed Induction Generator Based on Multi-Level Fusion and Measurement of Back-to-Back Converter Current Signal
M. Kamarzarrin, M. H. Refan, P. Amiri, A. Dameshghi,
Volume 18, Issue 2 (6-2022)
Abstract
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%.

An Effective Hilbert–Huang Transform-Based Approach for Partial Demagnetization Fault Diagnosis in Double-Rotor Double-Sided Stator Structure Axial Flux Permanent Magnet Generator under Various Load and Speed Conditions
Makan Torabi, Yousef Alinejad-Beromi,
Volume 19, Issue 4 (12-2023)
Abstract
A double-sided axial flux Permanent Magnet (PM) generator which can be directly driven by small-scale low-speed turbines is highly suitable for use in renewable energy generation systems. Partial demagnetization is a failure occurring under the high thermal operation of a Permanent Magnet machine. This paper focuses on partial demagnetization fault diagnosis in a double-rotor double-sided axial flux PM generator using stator currents analysis under time-varying conditions. One of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned problems, this paper adopts a novelty detection algorithm based on the Hilbert–Huang transform for fault diagnosis. This approach relies on two steps: estimating the intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) and computing the instantaneous amplitude (IA) and Instantaneous Frequency (IF) of IMFs using the Hilbert transform. The more significant IMFs are determined using the Hilbert spectrum, which is applied for accurate fault diagnosis. The Partial demagnetization severity can be evaluated based on the IMF’s energy value. The theoretical basis of the proposed method is presented. The effectiveness of the proposed method is verified by a series of simulation and experimental tests under different conditions.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.