TY - JOUR
T1 - Adaptive power transformer lifetime predictions through machine learning and uncertainty modelling in nuclear power plants
AU - Aizpurua, Jose Ignacio
AU - McArthur, Stephen D. J.
AU - Stewart, Brian G.
AU - Lambert, Brandon
AU - Cross, James G.
AU - Catterson, Victoria M.
N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2019/6/30
Y1 - 2019/6/30
N2 - The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the non-linearities of the thermal model and improves the prediction accuracy amongst a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a Particle Filtering framework to improve thermal modelling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant.
AB - The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the non-linearities of the thermal model and improves the prediction accuracy amongst a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a Particle Filtering framework to improve thermal modelling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant.
KW - condition assessment
KW - forecasting
KW - prognostics and health management
KW - sensitivity
KW - transformers
UR - https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=41
U2 - 10.1109/TIE.2018.2860532
DO - 10.1109/TIE.2018.2860532
M3 - Article
SN - 0278-0046
VL - 66
SP - 4726
EP - 4737
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 6
ER -