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Decomposition Characteristics of SF 6 and Partial Discharge Recognition under Negative DC Conditions

Author

Listed:
  • Ju Tang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Xu Yang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Gaoxiang Ye

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Qiang Yao

    (Chongqing Electric Power Research Institute, Chongqing Power Company, Chongqing 401123, China)

  • Yulong Miao

    (Chongqing Electric Power Research Institute, Chongqing Power Company, Chongqing 401123, China)

  • Fuping Zeng

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

Abstract

Four typical types of artificial defects are designed in conducting the decomposition experiments of SF 6 gas to obtain and understand the decomposition characteristics of SF 6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition characteristics of SF 6 in diagnosing the type and severity of insulation fault in DC SF 6 gas-insulated equipment. Experimental results show that the negative DC partial discharges caused by the four defects decompose the SF 6 gas and generate five stable decomposed components, namely, CF 4 , CO 2 , SO 2 F 2 , SOF 2 , and SO 2 . The concentration, effective formation rate, and concentration ratio of SF 6 decomposed components can be associated with the PD types. Furthermore, back propagation neural network algorithm is used to recognize the PD types. The recognition results show that compared with the concentrations of SF 6 decomposed components, their concentration ratios are more suitable as the characteristic quantities for PD recognition, and using those concentration ratios in recognizing the PD types can obtain a good effect.

Suggested Citation

  • Ju Tang & Xu Yang & Gaoxiang Ye & Qiang Yao & Yulong Miao & Fuping Zeng, 2017. "Decomposition Characteristics of SF 6 and Partial Discharge Recognition under Negative DC Conditions," Energies, MDPI, vol. 10(4), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:556-:d:96099
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    References listed on IDEAS

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    1. Abdullahi Abubakar Mas’ud & Ricardo Albarracín & Jorge Alfredo Ardila-Rey & Firdaus Muhammad-Sukki & Hazlee Azil Illias & Nurul Aini Bani & Abu Bakar Munir, 2016. "Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions," Energies, MDPI, vol. 9(8), pages 1-18, July.
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    Cited by:

    1. Ju Tang & Xu Yang & Dong Yang & Qiang Yao & Yulong Miao & Chaohai Zhang & Fuping Zeng, 2017. "Using SF 6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect," Energies, MDPI, vol. 10(8), pages 1-17, August.
    2. Yong Sung Cho & Tae Yoon Hong & Young Woo Youn & Jong Ho Sun & Se-Hee Lee, 2020. "Study on the Correlation between Partial Discharge Energy and SF 6 Decomposition Gas Generation," Energies, MDPI, vol. 13(18), pages 1-10, September.
    3. Gaoyang Li & Xiaohua Wang & Aijun Yang & Mingzhe Rong & Kang Yang, 2017. "Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation," Energies, MDPI, vol. 10(11), pages 1-20, November.

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