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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