Optimization of asset management of transformers based on a predictive health model of paper degradation

In this paper, a model-based predictive framework is proposed to optimize the operation and maintenance actions for power system equipment which operates in a changing environment of the future grid. In this framework, a predictive health model is proposed that predicts the health state of this equipment based on its operation and maintenance actions. In particular, this framework is used to predict the health state of transformers based on their usage and operating environment. The hot-spot temperature of the transformer is predicted from the expected loading of the transformer. Based on the hot-spot temperature predictions, the allowed loading limits of the transformers are determined. In the case of absence of the anticipated loading of the transformer, a maximum allowable loading limit of the transformer is estimated.