High‐accuracy localisation method for PD in transformers

Cai, Junyi; Zhou, Lijun; Hu, Junjie; Zhang, Chenqingyu; Liao, Wei; Guo, Lan-Ping

doi:10.1049/iet-smt.2019.0051

Cited by 12 publications

(1 citation statement)

References 26 publications

(37 reference statements)

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“…However, these methods cannot accurately locate the PD sources within the transformer. These problems have been investigated in [11] [12] utilizing intelligent techniques such as hybrid differential evolution (DE) algorithm and the particle swarm optimisation (PSO) algorithm and the results showed high accuracy of detection and localization.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Detection Based on Ultrasound Using Optimized Deep Learning Approach

Alshalawi,

Al-Ismail

2024

IEEE Access

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Electrical equipment is prone to different types of Partial Discharge (PD) failures that are varying between minor and severe level. In this paper, Three developed models for Convolution Neural Network (CNN) are proposed to detect and classify four different partial discharge types which are arcing, corona discharge, tracking, looseness as well as healthy equipment situation. Notably, the resulting models exhibited an impressive overall accuracy of more than 94%, which is particularly significant considering the inherent presence of noise in the real-world samples obtained as representative field failures. These findings underscore the robustness and effectiveness of the CNN models in accurately identifying PDs, despite the intricate challenges associated with real-world data.

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Section: Introductionmentioning

confidence: 99%