Summary
This paper provides an overview of an ongoing initiative to develop and implement digital twins for power transformers, highlighting recent progress through illustrative application examples.
Read more Read lessIntroduced first is a diagnostics and prognostics framework based on a multi-state model, where each node represents a physical state characterised by detectable signs or symptoms identified through monitoring or inferred using correlated data and virtual sensing techniques.
Applications of artificial intelligence are then explored, including a case study of the use of deep learning to detect anomalies in on-load tap changers through vibro-acoustic monitoring.
Also discussed are advancements in dissolved gas analysis (DGA) interpretation, with international methodologies implemented in software tools to assess the condition of the active part, bushings, and tap changers. Further sections address thermal behaviour, moisture migration, and insulation ageing, showcasing examples related to cooling system performance monitoring and recent developments in insulation condition assessment. The paper concludes with an examination of external components, proposing a methodology to enhance understanding of bushing degradation and improve their condition assessment.
Additional informations
| Publication type | Session Materials |
|---|---|
| Reference | A2_11862_2026 |
| Publication year | |
| Publisher | CIGRE |
| Country | Canada |
| Study committees | |
| File size | 2 MB |
| Price for non member | 30 € |
| Price for member | 30 € |
Authors
PICHER Patrick - Hydro-Québec; KIROUAC Mathieu - Hydro-Québec; ZEMOURI Riyad - Hydro-Québec; ARROYO Oscar - Hydro-Québec; RODRIGUEZ Mariela - Hydro-Québec; DUVAL Michel - Hydro-Québec; SENECHAL Nelly - Hydro-Québec; CEA Marta - Hydro-Québec; SERVENTI Alessandra - Hydro-Québec; GAUVIN Michel - Hydro-Québec; PROULX Stéphane - Hydro-Québec; LALONDE Mathieu - Hydro-Québec; FRENETTE Eric - Hydro-Québec; BIZIER Germain - Hydro-Québec
Keywords
Power transformers, digital twin, digitalisation, monitoring, condition assessment, artificial intelligence
