Detection and classification of disturbances in the islanded micro-grid by using wavelet transformation and feature extraction algorithm

Detection and classification of disturbances in the islanded micro-grid by using wavelet transformation and feature extraction algorithm

Digital signal processing (DSP) methods have been used by many researchers for detection and classification of transient disturbances because of their fast and powerful abilities to recognise waveform distortions. However, some DSP methods such as the wavelet transformation (WT) show less accuracy w...

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Bibliographic Details
Main Authors: Yunqi Wang, Ahmed Raza, Faisal Parvez Mohammed, Jayashri Ravishankar, Toan Phung
Format: Article
Language:English
Published: Wiley 2019-06-01
Series:The Journal of Engineering
Subjects:
digital signal processing chips
signal processing
power distribution faults
distributed power generation
power supply quality
signal classification
feature extraction
wavelet transforms
medical signal processing
entropy
classification
wavelet transformation
feature extraction algorithm
digital signal processing methods
detection
transient disturbances
fast abilities
powerful abilities
waveform distortions
dsp methods
noisy real data
disturbance features
wavelet domain
signal energy
noisy signals
power systems
islanded microgrid
wavelet-based normalised renyi entropy
disturbance types
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9362

Internet

https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9362

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