On Particle Filtering for Power Transformer Remaining Useful Life Estimation

The power transformer is a key element in a power system and its condition needs to be monitored and evaluated. However, subject to electrical, thermal, and mechanical stresses, the condition of a power transformer can eventually deteriorate causing the loss of the transformer's useful life. Utilizing various condition monitoring data of the transformer, this paper applies a state-space model method to the transformer's remaining useful life estimation. In the state-space model, a state dynamic equation considering the transformer aging mechanism is developed. Three measurement equations using different types of condition monitoring data are established. To solve the nonlinear state-space model, a particle filtering approach is applied. The posterior probability density function of the state variable obtained from the particle filtering is used to determine the transformer's remaining useful life. A number of case studies are carried out to demonstrate the applicability of the proposed method.