Chen Chen, Lijun Yang, Wu Zhao, 等. Study on Impurity Particle Detection in Transformer Oil Based on Microflow Imaging Method[J]. 高电压(英文), 2026,11(4).
Chen Chen, Lijun Yang, Wu Zhao, et al. Study on Impurity Particle Detection in Transformer Oil Based on Microflow Imaging Method[J]. High Voltage, 2026, 11(4).
Chen Chen, Lijun Yang, Wu Zhao, 等. Study on Impurity Particle Detection in Transformer Oil Based on Microflow Imaging Method[J]. 高电压(英文), 2026,11(4).DOI: 10.1049/hve2.70167.
Chen Chen, Lijun Yang, Wu Zhao, et al. Study on Impurity Particle Detection in Transformer Oil Based on Microflow Imaging Method[J]. High Voltage, 2026, 11(4).DOI: 10.1049/hve2.70167.
Study on Impurity Particle Detection in Transformer Oil Based on Microflow Imaging Method
摘要
The concentration of particles in transformer oil has a significant influence on its electrical performance
making oil cleanliness a critical parameter in the operation and maintenance of power transformers. However
the light obstruction (LO) method recommended by current standards has inherent limitations
including low sensitivity to irregular particles and the inability to characterise particle morphology. To address these deficiencies
this study introduces microflow imaging (MFI) as an alternative technique for insulating oil analysis. MFI is a high-precision method based on dynamic microscopic imaging that enables simultaneous measurement of particle concentration
size distribution and morphological characteristics. In this work
both LO and MFI methods were applied to oils collected from in-service transformers and accelerated thermal ageing of oil-paper insulation. For relatively clean oil
the ratio of particle counts measured by MFI to those obtained by LO ranged from 0.48 to 1.26
demonstrating good consistency. However
as contamination levels increased
the particle count detected by MFI reached up to 5 times that measured by LO. Notably
accelerated thermal ageing of oil–paper insulation at 130°C for 60 days resulted in an ∼28.94% increase in the proportion of elongated particles
with the total particle count measured by MFI being ∼4.82 times that obtained using LO. These results demonstrate that the LO method tends to underestimate particle quantities in oils with high concentrations of particles
particularly when irregular particles are present. By contrast
MFI exhibits significantly higher detection sensitivity. This study is the first to systematically demonstrate the technical advantages of MFI in evaluating particle contamination in transformer oil
providing a novel detection method for precise diagnostics of insulation conditions in power equipment.
Abstract
The concentration of particles in transformer oil has a significant influence on its electrical performance
making oil cleanliness a critical parameter in the operation and maintenance of power transformers. However
the light obstruction (LO) method recommended by current standards has inherent limitations
including low sensitivity to irregular particles and the inability to characterise particle morphology. To address these deficiencies
this study introduces microflow imaging (MFI) as an alternative technique for insulating oil analysis. MFI is a high-precision method based on dynamic microscopic imaging that enables simultaneous measurement of particle concentration
size distribution and morphological characteristics. In this work
both LO and MFI methods were applied to oils collected from in-service transformers and accelerated thermal ageing of oil-paper insulation. For relatively clean oil
the ratio of particle counts measured by MFI to those obtained by LO ranged from 0.48 to 1.26
demonstrating good consistency. However
as contamination levels increased
the particle count detected by MFI reached up to 5 times that measured by LO. Notably
accelerated thermal ageing of oil–paper insulation at 130°C for 60 days resulted in an ∼28.94% increase in the proportion of elongated particles
with the total particle count measured by MFI being ∼4.82 times that obtained using LO. These results demonstrate that the LO method tends to underestimate particle quantities in oils with high concentrations of particles
particularly when irregular particles are present. By contrast
MFI exhibits significantly higher detection sensitivity. This study is the first to systematically demonstrate the technical advantages of MFI in evaluating particle contamination in transformer oil
providing a novel detection method for precise diagnostics of insulation conditions in power equipment.