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Judgment of oil-filteration effect for superhydrophobic film based on machine learning
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Zenghui ZHANG1, Dewang FENG2, Yunxiao ZHANG3, Tianfeng ZHANG3, Zhengnan CHI3, Tao LIN4
Insulating Materials | 2023, 56(12) : 98 - 103
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Insulating Materials | 2023, 56(12): 98-103
• Advanced Electrical Materials for Large Capacity Offshore Wind Power Transmission •
Judgment of oil-filteration effect for superhydrophobic film based on machine learning
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Zenghui ZHANG1, Dewang FENG2, Yunxiao ZHANG3, Tianfeng ZHANG3, Zhengnan CHI3, Tao LIN4
Affiliations
  • 1School of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fujian 35000, China
  • 2School of Computer and Information, Fujian Agriculture and Forestry University, Fujian 35000, China
  • 3School of Electrical Engineering and Automation, Fuzhou University, Fujian 350108, China
  • 4Super High Voltage Branch of State Grid Fujian Electric Power Co., Ltd., Fujian 350013, China
Published: 2023-12-20 doi: 10.16790/j.cnki.1009-9239.im.2023.12.013
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In the long-term operation process, due to the ageing of transformer oil for offshore wind power, the moisture and other impurities will be produced, which may cause transformer insulation failure, resulting in economic losses and safety accidents. Thus, it is urgent to propose an effective method of transformer oil filteration and judgment to improve the transformer oil performance and evaluate its ageing status. In this paper, the oil filter film was conducted superhydrophobic modification, and the effects of the number of oil filtration, the type of film, and the superhydrophobic modification on the properties of oil before and after filtration were investigated. The health classification model of transformer oil was established by support vector machine algorithm. In addition, a new method based on machine learning was proposed to evaluate the oil filtering effect of superhydrophobic film. The results show that the oil filtering performance of the film after superhydrophobic treatment is improved greatly. Especially after three times of modified organic film filtration, the comprehensive performance of transformer oil has significantly improved, meeting the applicable standard of transformer oil. Compared with several algorithms, the model built by support vector algorithm has the highest accuracy of 84.8%.

transformer oil  /  superhydrophobic  /  electrical performance  /  support vector machine  /  health assessment
Zenghui ZHANG, Dewang FENG, Yunxiao ZHANG, Tianfeng ZHANG, Zhengnan CHI, Tao LIN. Judgment of oil-filteration effect for superhydrophobic film based on machine learning[J]. Insulating Materials, 2023 , 56 (12) : 98 -103 . DOI: 10.16790/j.cnki.1009-9239.im.2023.12.013
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Year 2023 volume 56 Issue 12
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doi: 10.16790/j.cnki.1009-9239.im.2023.12.013
  • Receive Date：2023-05-23
  • Online Date：2025-11-24
  • Published：2023-12-20
Article Data
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History
  • Received：2023-05-23
  • Revised：2023-07-10
Funding
Affiliations
    1School of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fujian 35000, China
    2School of Computer and Information, Fujian Agriculture and Forestry University, Fujian 35000, China
    3School of Electrical Engineering and Automation, Fuzhou University, Fujian 350108, China
    4Super High Voltage Branch of State Grid Fujian Electric Power Co., Ltd., Fujian 350013, China
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https://castjournals.cast.org.cn/joweb/jycl/EN/10.16790/j.cnki.1009-9239.im.2023.12.013
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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