Prognostics & health management methods & tools for transformer condition monitoring in smart grids

Jose Ignacio Aizpurua, Brian G. Stewart, Stephen D. J. McArthur, Unai Garro, Eñaut Muxika, Mikel Mendicute, V. M. Catterson, Ian P. Gilbert, Luis del Rio

Research output: Contribution to conferencePaper

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Abstract

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.
Original languageEnglish
Number of pages12
Publication statusPublished - 7 Oct 2019
EventIEEE 6th International Advanced Research Workshop on Transformers (ARWtr2019) - Cordoba, Spain
Duration: 7 Oct 20199 Oct 2019
http://arwtr2019.webs.uvigo.es/

Conference

ConferenceIEEE 6th International Advanced Research Workshop on Transformers (ARWtr2019)
Abbreviated titleARWtr 2019
CountrySpain
CityCordoba
Period7/10/199/10/19
Internet address

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Power Transformer
Smart Grid
Condition Monitoring
Power transformers
Condition monitoring
Transformer
Lifetime
Health
Dynamic loads
Forecasting
Planning
Uncertainty
Dynamic Load
State Estimation
State estimation
Maintenance
Filtering
Grid
Health management
Management methods

Keywords

  • condition monitoring
  • probabilistic forecasting
  • transformer
  • prognostics
  • diagnostics

Cite this

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.
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. / 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.12 p.
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title = "Prognostics & health management methods & tools for transformer condition monitoring in smart grids",
abstract = "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.",
keywords = "condition monitoring, probabilistic forecasting, transformer, prognostics, diagnostics",
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Aizpurua, JI, Stewart, BG, McArthur, SDJ, Garro, U, Muxika, E, Mendicute, M, Catterson, VM, Gilbert, IP & 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, 7/10/19 - 9/10/19, .

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 conferencePaper

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 -

Aizpurua JI, Stewart BG, McArthur SDJ, Garro U, Muxika E, Mendicute M et al. Prognostics & health management methods & tools for transformer condition monitoring in smart grids. 2019. Paper presented at IEEE 6th International Advanced Research Workshop on Transformers (ARWtr2019), Cordoba, Spain.