On Particle Filtering for Power Transformer Remaining Useful Life Estimation

• Published in: IEEE transactions on power delivery Vol. 33; no. 6; pp. 2643 - 2653
• Main Authors: Li, Shuaibing, Ma, Hui, Saha, Tapan Kumar, Yang, Yan, Wu, Guangning
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aging; Condition assessment; Condition monitoring; Conditional probability; Filtration; Mathematical model; Oil insulation; particle filtering; power transformer; Power transformer insulation; Probability density functions; remaining useful life; State space models; State variable; state-space model; Transformers; Useful life
• ISSN: 0885-8977; 1937-4208
• DOI: 10.1109/TPWRD.2018.2807386

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.