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Intelligent Diagnosis Method for Mechanical Faults of High-Voltage Shunt Reactors Based on Vibration Measurements

Authors :
Pengfei Hou
Hongzhong Ma
Ping Ju
Source :
Machines, Vol 10, Iss 8, p 627 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Aiming at the difficulty of accurately identifying latent mechanical faults inside high-voltage shunt reactors (HVSRs), this paper proposes a new method for HVSR state feature extraction and intelligent diagnosis. The method integrates a modified complementary ensemble empirical mode decomposition (CEEMD)–permutation entropy–CEEMD (MCPCEEMD) method, mutual information theory (MI), multiscale fuzzy entropy (MFE), and an improved grasshopper optimization algorithm to optimize the probabilistic neural network (IGOA-PNN) model. First, we introduce MCPCEEMD for suppressing modal aliasing to decompose the HVSR raw vibration signals. Then, the correlation degree between the obtained intrinsic mode function (IMF) components and the HVSR original vibration signals is judged through MI, and the IMF with the highest correlation is selected for feature extraction. Furthermore, this study uses MFE to quantify the selected IMF. Finally, we employ piecewise inertial weights to improve GOA to select the best smoothing factor for PNN, and use the optimized IGOA-PNN model to identify feature subsets. The experimental results show that the proposed method can successfully diagnose different types and degrees of HVSR mechanical faults, and the identification accuracy rate reaches more than 98%. The high recognition accuracy of the proposed method is helpful for the state detection and field application of HVSRs.

Details

Language :
English
ISSN :
10080627 and 20751702
Volume :
10
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Machines
Publication Type :
Academic Journal
Accession number :
edsdoj.8ee5723676d048ab892034837e5aeb46
Document Type :
article
Full Text :
https://doi.org/10.3390/machines10080627