The assessment method of transformer oil-paper insulation state based on PSO-ELM

Abstract

Oil-immersed power transformer is an important part of power grid, and its reliable operation plays a vital role in pomler system security. Aiming at the problem that the insulation state of transformer cannot be assessed quantitatively after long-term operation, the accelerated aging and damp tests of oil-paper insulation model are carried out in this paper. The influence of aging and damp of oil-paper insulation on its recovery voltage curves is explored. The particle swarm optimization (PSO) is used to improve the parameter prediction method of extreme learning machine (ELM), which realizes the quantitative assessment of aging and moisture of oil-paper insulation based on the characteristic parameters of the recovery voltage curve. By comparing the physical and chemical performance analysis of oil-paper insulation models, it is shown that the prediction accuracy of PSO-ELM method is much higher than that of traditional ELM method. The absolute error range for predicting the moisture content of oil-paper insulation the degree of polymerization (DP) of pressboard is less than ±0.4% or ±30, respectively.

Keywords

• oil-immersed transformer
• oil-paper insulation
• recovery voltage
• partical swarm optimization-extreme learning machine (pso-elm) algorithm
• state assessment
• non-destructive testing