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Volume 19, Issue 1
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973
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Abstract

Introduction

Partial discharge in oil-paper insulated bushings represents a significant fault type in converter transformers during operation. Statistical analysis reveals that approximately 20% of insulation-related failures in converter transformers originate from partial discharges in bushings. When not detected promptly, these partial discharges can lead to insulation breakdown within 3–6 months. Each failure incident typically causes an 8–12 hour power interruption, resulting in substantial economic losses ranging from ¥500,000 to ¥800,000. This research addresses the critical challenge of precise partial discharge localization in bushings to enable effective casing maintenance.

Methods

The study initially developed an electromagnetic wave propagation simulation model for partial casing discharge to analyze the dynamic electromagnetic wave propagation process comprehensively. Subsequently, the Time Difference of Arrival (TDOA) localization algorithm underwent optimization through integration with the neural network, simulated annealing algorithm, and Bayesian algorithm.

Results

Simulation results demonstrate that electromagnetic wave signals propagate into outer space as spherical waves through the oil gap between the flange end screen and the upper casing section. The maximum electric field intensity direction exhibits substantial variations between the casing surface and the far end. The enhanced algorithms demonstrate improved localization accuracy. The neural network-based TDOA achieves a reduced Mean Absolute Percentage Error (MAPE) of 5%, with over 80% of errors contained within 0.5 units of the actual position in each coordinate direction. The Bayesian-based improvement demonstrates a MAPE of 8%, with 70% of errors within 0.8 units. The simulated annealing-based enhancement achieves a MAPE of 6%, with 85% of errors within 0.6 units.

Discussion

Based on the characteristics of the electromagnetic wave signal propagation process in the internal and external space of the oil-paper insulation sleeve, this article further improves the of the TDOA positioning algorithm based on the neural network.

Conclusion

The enhanced TDOA localization algorithm, incorporating neural network, simulated annealing, and Bayesian algorithms, successfully improves the accuracy of partial discharge localization in bushings.

© 2026 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-05-22
2025-12-09

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Improved Time Difference of Arrival Algorithm for Partial Discharge Localization in Converter Transformer Bushings
Recent Advances in Electrical & Electronic Engineering 19, E23520965381061 (2026); https://doi.org/10.2174/0123520965381061250414111559
/content/journals/raeeng/10.2174/0123520965381061250414111559
/content/journals/raeeng/10.2174/0123520965381061250414111559
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  • Article Type:     Research Article
Keyword(s): localization algorithms; machine learning; partial discharges; RF signals; time difference of arrival; Transformer bushings

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