Highly Sensitive Micro-Optical Fiber Probe for Power Transformer Partial Discharge Detection

Partial discharge (PD) detection is crucial for ensuring the safe operation of power transformers. Optical fiber sensors (OFS), with advantages of electromagnetic interference (EMI) resistance, inherent safety, and suitability for embedded installation, demonstrate significant promise for PD detection. The micro-optical fiber probe (MOFP) with compact size (diameter 10 mm, height 4.9 mm) is proposed, which ensures sensitivity while offering the convenience and versatility required for embedded installation. The implementation of the heterodyne interferometric demodulation topology is employed to enhance stability, while the optimization of system parameter combinations leads to an improvement in the detection signal-tonoise ratio (SNR) by 24.7 dB. In the lab, the developed OFS achieves a 62% and 223.3% improvement in the SNR for detecting ultrasonic signals propagating in solid and in oil, respectively, compared to the typical PZT (R15α). In the field, the OFS was effectively installed inside the power transformer and detected PD ultrasonic signals, addressing the issues of EMI with HFCT and the installation difficulty and low sensitivity of PZT. The results substantiate that the developed OFS provides a novel, reliable and effective approach for PD detection in power transformers.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Power Delivery
Published
2025-04-18
DOI
https://doi.org/10.1109/tpwrd.2025.3562335
Citations
6
Primary Topic
Advanced Fiber Optic Sensors
Type
article
Field-Weighted Citation Impact
4.38
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Highly Sensitive Micro-Optical Fiber Probe for Power Transformer Partial Discharge Detection

Jun Jiang, Xiaohan Li, Jian Shao, Ruizhi Wang et al.
6 citations
IEEE Transactions on Power Delivery
Advanced Fiber Optic Sensors
4.38
article

Highly Sensitive Micro-Optical Fiber Probe for Power Transformer Partial Discharge Detection

Jun Jiang, Xiaohan Li, Jian Shao, Ruizhi Wang, Chaoyue Zhang
article en
6 citations

Abstract

Partial discharge (PD) detection is crucial for ensuring the safe operation of power transformers. Optical fiber sensors (OFS), with advantages of electromagnetic interference (EMI) resistance, inherent safety, and suitability for embedded installation, demonstrate significant promise for PD detection. The micro-optical fiber probe (MOFP) with compact size (diameter 10 mm, height 4.9 mm) is proposed, which ensures sensitivity while offering the convenience and versatility required for embedded installation. The implementation of the heterodyne interferometric demodulation topology is employed to enhance stability, while the optimization of system parameter combinations leads to an improvement in the detection signal-tonoise ratio (SNR) by 24.7 dB. In the lab, the developed OFS achieves a 62% and 223.3% improvement in the SNR for detecting ultrasonic signals propagating in solid and in oil, respectively, compared to the typical PZT (R15α). In the field, the OFS was effectively installed inside the power transformer and detected PD ultrasonic signals, addressing the issues of EMI with HFCT and the installation difficulty and low sensitivity of PZT. The results substantiate that the developed OFS provides a novel, reliable and effective approach for PD detection in power transformers.

IEEE Transactions on Power DeliveryVol. 40(4)
Shanghai Electric (China) (CN), Nanjing University of Aeronautics and Astronautics (CN)
Affordable and clean energy
Openalex Percentile: Top 6%
Advanced Fiber Optic Sensors
4.38
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.