Deep Convolutional Stack Autoencoder of Process Adaptive VMD Data With Robust Multikernel RVFLN for Power Quality Events Recognition

In this article, an improved particle swarm optimization (PSO)-based variational mode decomposition (VMD) is proposed to compute the most informative band-limited intrinsic mode function (BLIMF) of highly nonstationary single as well as combined power quality events (PQEs). A novel reduced deep conv...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 70; S. 1 - 12
Hauptverfasser:	Sahani, Mrutyunjaya, Dash, Pradipta Kishore
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive algorithms Artificial neural networks Autoencoder Computational modeling Convolution deep convolutional neural network (DCNN) Embedded systems Feature extraction Feature recognition field programmable gate array Field programmable gate arrays Harmonic analysis Machine learning Microprocessors Noise measurement parameter adaptive variational mode decomposition (PAVMD) Particle swarm optimization power quality analysis random vector functional link network Real-time systems Robustness Transient analysis
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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