Real-time Condition Monitoring and Asset Management of Oil- Immersed Power Transformers
| dc.contributor.author | Jin, Lan Maggie | |
| dc.contributor.supervisor | Ahmed Abu-Siada | en_US |
| dc.contributor.supervisor | Dowon Kim | en_US |
| dc.date.accessioned | 2024-07-09T02:36:15Z | |
| dc.date.available | 2024-07-09T02:36:15Z | |
| dc.date.issued | 2024 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/95487 | |
| dc.description.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. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Real-time Condition Monitoring and Asset Management of Oil- Immersed Power Transformers | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | MPhil | en_US |
| curtin.department | School of Electrical Engineering, Computing and Mathematical Sciences | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Science and Engineering | en_US |
| curtin.contributor.orcid | Jin, Lan Maggie [0000-0002-4753-9543] | en_US |