2021
DOI: 10.3390/en14165040
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A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids

Abstract: Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert–Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-f… Show more

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Cited by 18 publications
(7 citation statements)
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“…When in use, the frequency and time domain of the vibration signal can be related to each other, and then the signal characteristics contained in the time domain signal can be displayed and used for analysis. Therefore, the Fourier transform has been widely used [ 25 , 26 ].…”
Section: Time Domain and Frequency Domain Analysismentioning
confidence: 99%
“…When in use, the frequency and time domain of the vibration signal can be related to each other, and then the signal characteristics contained in the time domain signal can be displayed and used for analysis. Therefore, the Fourier transform has been widely used [ 25 , 26 ].…”
Section: Time Domain and Frequency Domain Analysismentioning
confidence: 99%
“…As can be seen, it is impossible to figure out specific patterns and differences between each type from the time domain. Since the signal of the wet clutch is more chaotic than other rotating machinery [ 23 ], the Hilbert–Huang transform is introduced here for signal processing because it has excellent characteristics to analyze non-stationary and non-linear signals [ 26 ]. This technique was improved by Huang, who introduced empirical mode decomposition (EMD) before the signal undergoes Hilbert transform [ 27 ].…”
Section: Signal Processing: Hilbert Spectrum and Time-frequency Entropymentioning
confidence: 99%
“…Interval type-2 (T2) FLS, which includes membership functions (MFs) of fuzzy intervals, was proposed and proved to be more resistant to uncertainties than type-1 fuzzy logic. Interval T2FLS has many applications, the most recent of which includes fault detection [16,17], robotic control [18], medical diagnose [19], prediction problems [20], risk diagnosis [21] and financial investment [22]. The general T2FLS was introduced to improve IT2FLS performance in various applications.…”
Section: Introductionmentioning
confidence: 99%