An approach on lifetime estimation of distribution transformers based on degree of polymerization

Ariannik, Mohamadreza; Razi-Kazemi, Ali A.; Lehtonen, Matti

doi:10.1016/j.ress.2020.106881

Cited by 19 publications

(11 citation statements)

References 28 publications

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“…( 16) Eq. ( 18) standard IEEE C57.91 [49], and its variations [50,51]. Configuration #2 draws measurement errors from the Gaussian distribution for topoil temperature and load as in [40,52,53].…”

Section: Analysis Methodologymentioning

confidence: 99%

“…Transformer lifetime estimation approaches have been focused on the integration of collected measurement into a lifetime estimation model [40,[50][51][52][53]. Bicen et al implemented a transformer lifetime monitoring model based on annual load factors [50] and Ariannik et al developed a lifetime estimation model for transformers based on the degree of polymerization and water levels [51]. These models can convey predictive information based on deterministic experimental equations.…”

Section: Case Study: Transformer Probabilistic Lifetime Estimation Un...mentioning

confidence: 99%

“…The insulation end-of-life is governed by the winding temperature, which in turn, is determined by the load current that flows through the transformer along with ambient temperature and transformer-specific parameters. The influence of humidity in the DP is a studied phenomena [51]. However, this work focuses on the experimental model given in the standard IEEE C57.91 [49] due to the unavailability of water level measurements.…”

Section: Transformer Degradation Modellingmentioning

confidence: 99%

“…(10), (11)] are widely employed to estimate the lifetime of transformers, e.g. [20,50,51]. However, these formulations do not integrate different sources of uncertainty.…”

Section: Sources Of Uncertaintymentioning

confidence: 99%

See 3 more Smart Citations

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

Aizpurua

Stewart²,

McArthur³

et al. 2022

Reliability Engineering & System Safety

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Section: Analysis Methodologymentioning

confidence: 99%

Section: Case Study: Transformer Probabilistic Lifetime Estimation Un...mentioning

confidence: 99%

Section: Transformer Degradation Modellingmentioning

confidence: 99%

“…(10), (11)] are widely employed to estimate the lifetime of transformers, e.g. [20,50,51]. However, these formulations do not integrate different sources of uncertainty.…”

Section: Sources Of Uncertaintymentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

Aizpurua

Stewart²,

McArthur³

et al. 2022

Reliability Engineering & System Safety

View full text Add to dashboard Cite

“…HST estimation models have been integrated with transformer lifetime models to predict the transformer RUL. These models, specified as deterministic and steady-state models, have been used for transformer prognostics predictions in distribution networks [9], [10], electric vehicles [11], photovoltaic (PV) generation [12], [13], [14], [15], wind energy [16], nuclear power plants [17], [18] and smart grid infrastructure [19]. However, these formulations do not integrate uncertainties present in the lifetime prediction [9], [10], [11], [12], [13], [14], [15], [16], [19], and result in point forecasts, i.e.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Transformer Prognostics Framework for Enhanced Probabilistic Predictions in Renewable Energy Applications

Aizpurua

Ramirez

Lasa³

et al. 2023

IEEE Trans. Power Delivery

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The intermittent nature of renewable energy sources (RESs) hamper their integration to the grid. The stochastic and rapid-changing operation of RES technologies impact on power equipment reliability. Transformers are key integrative assets of the power grid and it is crucial to monitor their health for the reliable integration of RESs. Existing models to transformer lifetime estimation are based on point forecasts or steady-state models. In this context, this paper presents a novel hybrid transformer prognostics framework for enhanced probabilistic predictions in RES applications. To this end, physics-based transient thermal models and probabilistic forecasting models are integrated using an error-correction configuration. The thermal prediction model is then embedded within a probabilistic prognostics framework to integrate forecasting estimates within the lifetime model, propagate associated uncertainties and predict the transformer remaining useful life with prediction intervals. Prediction intervals vary for each prediction according to the propagated uncertainty and they inform about the confidence of the model in the predictions. The proposed approach is tested and validated with a floating solar power plant case study. Results show that, from the insulation degradation perspective, there may be room to extend the transformer useful life beyond initial lifetime assumptions.

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A hybrid ensemble learning approach for the aging-dependent reliability prediction of power transformers

et al. 2023

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An approach on lifetime estimation of distribution transformers based on degree of polymerization

Cited by 19 publications

References 28 publications

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

Hybrid Transformer Prognostics Framework for Enhanced Probabilistic Predictions in Renewable Energy Applications

A hybrid ensemble learning approach for the aging-dependent reliability prediction of power transformers

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