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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 12
Main Authors: Sahani, Mrutyunjaya, Dash, Pradipta Kishore
Format: Journal Article
Language:English
Published: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 convolutional neural network (RDCNN) embedded with stack autoencoder, that is, RDCSAE structure is introduced to extract the most discriminative unsupervised feature data by importing the selected BLIMF of parameter-adaptive VMD (PAVMD) algorithm. A supervised robust multikernel random vector functional link network (RMRVFLN) method is proposed to further train the unsupervised features combined with the deep convoluted Fourier privileged data for the recognition of complex PQEs accurately. Automatic computation of minimum overlapped descriptive features, unified complex feature learning framework, outstanding recognition accuracy, robust antinoise performance, and quick PQEs recognition time prove the superiority of the proposed PAVMD-RDCSAE-RMRVFLN method over RDCNN, PAVMD-RDCNN, PAVMD-RDCSAE, PAVMD-RDCNN-RMRVFLN, and PAVMD-RDCSAE-MRVFLN methods. Finally, the architecture of the proposed method is designed, implemented, and tested in a fast digital field-programmable gate array (FPGA) embedded processor to validate the feasibility, practicability, and performances for the online PQEs monitoring.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3054673