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- 저자 : Chaofei Gao (검색결과 2 건)
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Zan Wang,Chaofei Gao,Liwei Zheng,Jikun Ren,Wei Wang,Yushuai Zhang,Shijie Han 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.1
Ultrasonic signals will be generated when partial discharge occurs in internal insulation faults in large oil immersed power transformers: because the ultrasonic signal has strong anti-interference ability and has no direct electromagnetic contact with the equipment, it is widely used in transformer fault detection and positioning. In this paper, the fi nite element method (FEM) is used to simulate the ultrasonic signal in a 35 kV power transformer. The infl uence of transformer case on ultrasonic signal propagation is considered, and the propagation law of the ultrasonic signal inside the transformer is obtained. Fabry–Pérot (F–P) fi bre acoustic sensors with a centre frequency of 28 kHz were fabricated. A partial discharge detection test was carried out in a 35 kV transformer winding model using the F–P sensors. The test results show that the ultrasonic waveform detected by the F–P sensors are in good agreement with the simulation results, and the propagation of the ultrasonic wave inside the transformer is verifi ed. It lays a foundation for detecting and locating PDs in power transformer by F–P acoustic sensors.
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Research on Partial Discharge Pattern Recognition in GIS Based on EFPI Sensor
Wang Zan,Liu Zhongquan,Qiao Lili,Qian Dingdong,Chen Zhongxian,Gao Chaofei,Wang Wei 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.1
An EFPI fber optic ultrasonic sensor can be used for the detection and pattern recognition of partial discharge ultrasonic signals in Gas Insulated Switchgear. Compared with traditional piezoelectric sensors, it has many advantages, such as high sensitivity and strong anti-interference ability. Based on this, four typical PD models of the tip, metal particles, suspension and creeping surface were set up in the GIS cavity flled with 0.4 MPa SF6 gas, and the EFPI sensor was innovatively used to detect the discharge ultrasonic signal and extract the single ultrasonic pulse signal. The waveform features form a feature parameter database, and the probabilistic neural network algorithm and the support vector machine algorithm are used for pattern recognition and comparative analysis, respectively. The ultrasonic signal detected by the EFPI sensor has prominent features. On the basis of extracting the feature parameters, the two pattern recognition algorithms can achieve an average recognition rate of more than 85%, and the recognition efect of the support vector machine is better than that of the probabilistic neural network
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