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Detection of Micro-water Content in Transformer Oil Based on Multi Frequency Ultrasonic and Artificial Neural Network
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Huakun YANG1, Xianlong MA2, Shengpeng LI1, Yaquan LI1, Lixiong SUN1, Yang SU1, Qu ZHOU3
Insulating Materials | 2022, 55(4) : 114 - 120
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Insulating Materials | 2022, 55(4): 114-120
• Test and Analysis •
Detection of Micro-water Content in Transformer Oil Based on Multi Frequency Ultrasonic and Artificial Neural Network
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Huakun YANG1, Xianlong MA2, Shengpeng LI1, Yaquan LI1, Lixiong SUN1, Yang SU1, Qu ZHOU3
Affiliations
  • 1Baoshan Power Supply Bureau of Yunnan Power Grid Co., Ltd., Baoshan 678000, China
  • 2Electric Power Research Institute of Yunnan Power Co., Ltd., Kunming 650217, China
  • 3College of Engineering and Technology, Southwest University, Chongqing 400715, China
Published: 2022-04-20 doi: 10.16790/j.cnki.1009-9239.im.2022.04.017
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The micro-water content in transformer oil is an important factor to measure whether the transformer can operate stably for a long time. Based on multi-frequency ultrasonic detection combined with artificial neural network algorithm, a method for predicting micro-water content in transformer oil was proposed in this study. Firstly, the micro-water content in 210 groups of oils was determined by Carl Fischer titration. Secondly, 210 groups of oil samples were detected by multi-frequency ultrasound to analyze the relationship between micro-water content in oil samples and amplitude and phase signals in multi-frequency ultrasonic data. Finally, the original 242-dimensional multi-frequency ultrasonic data was reduced to 23-dimensional by PCA. Two prediction models for micro-water content in transformer oil based on PCA-GA-BPNN and PCA-PSO-GRNN were established by combining with BPNN and GRNN artificial neural networks as well as GA and PSO optimization algorithms. The prediction results were compared with the actual results. The results show that the forecast accuracy of both models is higher than 90%, which indicates that the method proposed in this study can effectively detect the moisture content in transformer oil.

transformer oil  /  micro-water content  /  multi-frequency ultrasound  /  artificial neural network  /  prediction model
Huakun YANG, Xianlong MA, Shengpeng LI, Yaquan LI, Lixiong SUN, Yang SU, Qu ZHOU. Detection of Micro-water Content in Transformer Oil Based on Multi Frequency Ultrasonic and Artificial Neural Network[J]. Insulating Materials, 2022 , 55 (4) : 114 -120 . DOI: 10.16790/j.cnki.1009-9239.im.2022.04.017
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Article Info
doi: 10.16790/j.cnki.1009-9239.im.2022.04.017
  • Receive Date：2021-06-03
  • Online Date：2025-12-22
  • Published：2022-04-20
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History
  • Received：2021-06-03
  • Revised：2021-07-28
Funding
Affiliations
    1Baoshan Power Supply Bureau of Yunnan Power Grid Co., Ltd., Baoshan 678000, China
    2Electric Power Research Institute of Yunnan Power Co., Ltd., Kunming 650217, China
    3College of Engineering and Technology, Southwest University, Chongqing 400715, China
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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
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Genus		 种数
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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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