Volume 19, Issue 4 (December 2023)                   IJEEE 2023, 19(4): 2672-2672 | Back to browse issues page


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torabi M, Alinejad-Beromi Y. 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. IJEEE 2023; 19 (4) :2672-2672
URL: http://ijeee.iust.ac.ir/article-1-2672-en.html
Abstract:   (530 Views)
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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Type of Study: Research Paper | Subject: Fault Detection and Diagnosis
Received: 2022/10/11 | Revised: 2023/12/30 | Accepted: 2023/09/13

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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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.