Abstract
| Original language | English |
|---|---|
| Number of pages | 12 |
| Publication status | Published - 7 Oct 2019 |
| Event | IEEE 6th International Advanced Research Workshop on Transformers (ARWtr2019) - Cordoba, Spain Duration: 7 Oct 2019 → 9 Oct 2019 http://arwtr2019.webs.uvigo.es/ |
Conference
| Conference | IEEE 6th International Advanced Research Workshop on Transformers (ARWtr2019) |
|---|---|
| Abbreviated title | ARWtr 2019 |
| Country | Spain |
| City | Cordoba |
| Period | 7/10/19 → 9/10/19 |
| Internet address |
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Keywords
- condition monitoring
- probabilistic forecasting
- transformer
- prognostics
- diagnostics
Cite this
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Prognostics & health management methods & tools for transformer condition monitoring in smart grids. / Aizpurua, Jose Ignacio; Stewart, Brian G.; McArthur, Stephen D. J.; Garro, Unai; Muxika, Eñaut; Mendicute, Mikel; Catterson, V. M.; Gilbert, Ian P.; del Rio, Luis.
2019. Paper presented at IEEE 6th International Advanced Research Workshop on Transformers (ARWtr2019), Cordoba, Spain.Research output: Contribution to conference › Paper
TY - CONF
T1 - Prognostics & health management methods & tools for transformer condition monitoring in smart grids
AU - Aizpurua, Jose Ignacio
AU - Stewart, Brian G.
AU - McArthur, Stephen D. J.
AU - Garro, Unai
AU - Muxika, Eñaut
AU - Mendicute, Mikel
AU - Catterson, V. M.
AU - Gilbert, Ian P.
AU - del Rio, Luis
PY - 2019/10/7
Y1 - 2019/10/7
N2 - Power transformers are critical assets for the correct and reliable operation of the power grid. However, the use of power transformers in the context of smart grids creates new challenges for efficient lifetime management and maintenance planning. The use of intermittent sources of energy and dynamic loads increases the sources of uncertainty and causes non-linear operation dynamics. In addition, the increased use of probabilistic forecasting models for the estimation of influential parameters such as temperature or load, influences the uncertainty associated with the transformer lifetime estimation. These variable operation mechanisms influence the operation and lifetime planning of power transformers. Accordingly, this paper presents a novel probabilistic health state estimation framework to improve the lifetime management of power transformers operated in smart grids through the integration of probabilistic forecasting models with Monte Carlo based Bayesian filtering methods.
AB - Power transformers are critical assets for the correct and reliable operation of the power grid. However, the use of power transformers in the context of smart grids creates new challenges for efficient lifetime management and maintenance planning. The use of intermittent sources of energy and dynamic loads increases the sources of uncertainty and causes non-linear operation dynamics. In addition, the increased use of probabilistic forecasting models for the estimation of influential parameters such as temperature or load, influences the uncertainty associated with the transformer lifetime estimation. These variable operation mechanisms influence the operation and lifetime planning of power transformers. Accordingly, this paper presents a novel probabilistic health state estimation framework to improve the lifetime management of power transformers operated in smart grids through the integration of probabilistic forecasting models with Monte Carlo based Bayesian filtering methods.
KW - condition monitoring
KW - probabilistic forecasting
KW - transformer
KW - prognostics
KW - diagnostics
UR - http://arwtr2019.webs.uvigo.es/
M3 - Paper
ER -