The framework for partial discharge recognition in power transformers by synergistically combining variational mode decomposition (VMD) optimization with deep spatiotemporal feature learning.

The framework for partial discharge recognition in power transformers by synergistically combining variational mode decomposition (VMD) optimization with deep spatiotemporal feature learning.

<p>The framework for partial discharge recognition in power transformers by synergistically combining variational mode decomposition (VMD) optimization with deep spatiotemporal feature learning.</p>

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Bibliographic Details
Main Author: Liang Chen (73736) (author)
Other Authors: You Wen (20646648) (author), Huaquan Su (22656301) (author), Ke Xiong (1814353) (author), Dani Chen (22656304) (author)
Published: 2025
Subjects:
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
varying load conditions
strong electromagnetic interference
preventing insulation failures
maintaining practical 0
existing methods struggle
dynamic feature fusion
automatically determining modes
8s processing time
power transformer based
modern power grids
lstm model featuring
penalty factor ),
adaptive signal processing
div >< p
research conclusively establishes
novel hybrid acoustic
lstm </ p
hierarchical attention mechanisms
accurate pd classification
power transformers
bidirectional lstm
tdoa ),
signal complexity
synergistic integration
study proposes
source defects
sample entropy
robust performance
partial discharge
parallel 1d
high noise
environmental noise
consistent 4
computational efficiency
classification accuracy
angular accuracy
8 heads
5 layers
256 units
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Description
Summary:<p>The framework for partial discharge recognition in power transformers by synergistically combining variational mode decomposition (VMD) optimization with deep spatiotemporal feature learning.</p>

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