Izenburua
Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case studyIkerketa taldea
Teoría de la señal y comunicacionesSistemas electrónicos de potencia aplicados al control de la energía eléctrica
Mecánica de fluidos
Beste instituzio
IkerbasqueUniversity of Strathclyde
National University of Ireland, Maynooth
Bertsioa
Postprinta
Eskubideak
© 2022 Elsevier Ltd. All rights reservedSarbidea
Sarbide bahituaArgitaratzailearen bertsioa
https://doi.org/10.1016/j.ress.2022.108676Non argitaratua
Reliability Engineering & System Safety Vol. 226. October, 2022Argitaratzailea
Elsevier Ltd.Gako-hitzak
condition monitoring
Probabilistic forecasting
Transformer
Prognostics ... [+]
Probabilistic forecasting
Transformer
Prognostics ... [+]
condition monitoring
Probabilistic forecasting
Transformer
Prognostics
uncertainty [-]
Probabilistic forecasting
Transformer
Prognostics
uncertainty [-]
Laburpena
The energy transition towards resilient and sustainable power plants requires moving from periodic health assessment to condition-based lifetime planning, which in turn, creates new challenges and opp ... [+]
The energy transition towards resilient and sustainable power plants requires moving from periodic health assessment to condition-based lifetime planning, which in turn, creates new challenges and opportunities for health estimation and prediction. Probabilistic forecasting models are being widely employed to predict the likely evolution of power grid parameters, such as weather prediction models and probabilistic load forecasting models, that precisely impact on the health state of power and energy components. These models synthesize forecasting knowledge and associated uncertainty information, and their integration within asset management practice would improve lifetime estimation under uncertainty through uncertainty-aware probabilistic predictions. Accordingly, this paper presents a probabilistic prognostics method for lifetime planning under uncertainty integrating data-driven probabilistic forecasting models with expert-knowledge based Bayesian filtering methods. The proposed concepts are applied and validated with power transformers operated in two different power generation systems and obtained results confirm that the proposed probabilistic transformer lifetime estimate aids in the decision-making process with informative lifetime distributions and associated confidence intervals. [-]