Real-time Condition Monitoring and Asset Management of Oil-
Immersed Power Transformers

Jin, Lan Maggie

Jin LM 2024 Public.pdf (6.380Mb)

Access Status

Open access

Authors

Jin, Lan Maggie

Date

2024

Supervisor

Ahmed Abu-Siada

Dowon Kim

Type

Thesis

Award

MPhil

Metadata

Show full item record

Faculty

Science and Engineering

School

School of Electrical Engineering, Computing and Mathematical Sciences

URI

http://hdl.handle.net/20.500.11937/95487

Collection

Curtin Theses

Abstract

This research pioneers a comprehensive asset management methodology utilizing solely online dissolved gas analysis. Integrating advanced AI algorithms, the model was trained and rigorously tested on real-world data, demonstrating its efficacy in optimizing asset performance and reliability.

Real-time Condition Monitoring and Asset Management of Oil- Immersed Power Transformers