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

Abstract

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.