A Particle Filtering Based Approach for Transformer Winding Degradation Prognostics

When the transformer works for a long time, its winding is gradually deteriorated with time, and the fault phenomena such as winding short circuit or circuit break lead to serious power supply accidents. Under high temperature conditions, this paper analyzes the degradation process of the winding, and determines that the resonant frequency can be used as testing index of its degradation process. Therefore, the resonant frequency is used to monitor the performance degradation state of the transformer winding and realize the advance prediction, which can effectively avoid accidents. Accurate prediction of system reliability is of plenty of importance to engineering systems for accomplishing the designate function and system safety management. As the concerned system is getting complicated and more sufficient health monitoring measurement is available, the traditional reliability prediction schemes resorting to only one kind of prediction approaches, model-based or data-driven, begin to show their limitations. This paper proposes a PF prognostic method by combining traditional model approaches. The effectiveness of the proposed method is verified by thermal degradation experiments. This method improves the reliability of power system and is conducive to the rapid development of smart grid.