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Research on prediction of breakdown voltage of insulating oil based on multi-frequency ultrasound and GWO-RF algorithm
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Hua YU, Hong LIU, Xuan WANG, Jizhong LIANG, Shuai LI
Insulating Materials | 2025, 58(1) : 130 - 136
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Insulating Materials | 2025, 58(1): 130-136
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Research on prediction of breakdown voltage of insulating oil based on multi-frequency ultrasound and GWO-RF algorithm
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Hua YU, Hong LIU, Xuan WANG, Jizhong LIANG, Shuai LI
Affiliations
  • State Grid Shanxi Electric Power Company Electric Power Science Research Institute, Taiyuan 030001, China
Published: 2025-01-20 doi: 10.16790/j.cnki.1009-9239.im.2025.01.016
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Insulating oil plays a critical role as a dielectric medium in reactors, and the breakdown voltage is a key indicator evaluating its insulating properties, which is closely related to the quality of insulating oil. In this paper, 155 reactor insulating oil samples were selected for experiments, which included the measurement of breakdown voltage and collection of multi-frequency ultrasound signals after propagation in the oil samples. The relationship between the breakdown voltage and the amplitude-frequency and phase-frequency responses of ultrasonic acoustic parameters was analyzed. A breakdown voltage prediction method was then proposed by combining multi-frequency ultrasound technology with a grey wolf optimizer (GWO) optimized random forest (RF) algorithm. The results show that the GWO-RF model achieves 4.04% of mean relative error and 95.96% of accuracy on the test set, and there is 20.25% of improvement in prediction accuracy compared to the unoptimized RF model. The proposed prediction model, which integrates multi-frequency ultrasound detection and GWO-RF optimization, demonstrates significant feasibility for predicting the breakdown voltage of insulating oil in reactor.

insulating oil  /  breakdown voltage  /  multi-frequency ultrasound  /  GWO-RF
Hua YU, Hong LIU, Xuan WANG, Jizhong LIANG, Shuai LI. Research on prediction of breakdown voltage of insulating oil based on multi-frequency ultrasound and GWO-RF algorithm[J]. Insulating Materials, 2025 , 58 (1) : 130 -136 . DOI: 10.16790/j.cnki.1009-9239.im.2025.01.016
Year 2025 volume 58 Issue 1
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Article Info
doi: 10.16790/j.cnki.1009-9239.im.2025.01.016
  • Receive Date:2024-03-11
  • Online Date:2025-11-05
  • Published:2025-01-20
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  • Received:2024-03-11
  • Revised:2024-03-26
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    State Grid Shanxi Electric Power Company Electric Power Science Research Institute, Taiyuan 030001, China
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https://castjournals.cast.org.cn/joweb/jycl/EN/10.16790/j.cnki.1009-9239.im.2025.01.016
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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