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.
|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
|Conference||IEEE 6th International Advanced Research Workshop on Transformers (ARWtr2019)|
|Abbreviated title||ARWtr 2019|
|Period||7/10/19 → 9/10/19|
- condition monitoring
- probabilistic forecasting
Aizpurua, J. I., Stewart, B. G., McArthur, S. D. J., Garro, U., Muxika, E., Mendicute, M., ... del Rio, L. (2019). Prognostics & health management methods & tools for transformer condition monitoring in smart grids. Paper presented at IEEE 6th International Advanced Research Workshop on Transformers (ARWtr2019), Cordoba, Spain.