Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions
Four typical types of artificial defects are designed in conducting the decomposition experiments of SF6 gas to obtain and understand the decomposition characteristics of SF6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition chara...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-04-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/10/4/556 |
| _version_ | 1811184523376852992 |
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| author | Ju Tang Xu Yang Gaoxiang Ye Qiang Yao Yulong Miao Fuping Zeng |
| author_facet | Ju Tang Xu Yang Gaoxiang Ye Qiang Yao Yulong Miao Fuping Zeng |
| author_sort | Ju Tang |
| collection | DOAJ |
| description | Four typical types of artificial defects are designed in conducting the decomposition experiments of SF6 gas to obtain and understand the decomposition characteristics of SF6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition characteristics of SF6 in diagnosing the type and severity of insulation fault in DC SF6 gas-insulated equipment. Experimental results show that the negative DC partial discharges caused by the four defects decompose the SF6 gas and generate five stable decomposed components, namely, CF4, CO2, SO2F2, SOF2, and SO2. The concentration, effective formation rate, and concentration ratio of SF6 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 SF6 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. |
| first_indexed | 2024-04-11T13:14:36Z |
| format | Article |
| id | doaj.art-49267c9652c94a71b18646e379a7ec37 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T13:14:36Z |
| publishDate | 2017-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-49267c9652c94a71b18646e379a7ec372022-12-22T04:22:27ZengMDPI AGEnergies1996-10732017-04-0110455610.3390/en10040556en10040556Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC ConditionsJu Tang0Xu Yang1Gaoxiang Ye2Qiang Yao3Yulong Miao4Fuping Zeng5School of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaChongqing Electric Power Research Institute, Chongqing Power Company, Chongqing 401123, ChinaChongqing Electric Power Research Institute, Chongqing Power Company, Chongqing 401123, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaFour typical types of artificial defects are designed in conducting the decomposition experiments of SF6 gas to obtain and understand the decomposition characteristics of SF6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition characteristics of SF6 in diagnosing the type and severity of insulation fault in DC SF6 gas-insulated equipment. Experimental results show that the negative DC partial discharges caused by the four defects decompose the SF6 gas and generate five stable decomposed components, namely, CF4, CO2, SO2F2, SOF2, and SO2. The concentration, effective formation rate, and concentration ratio of SF6 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 SF6 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.http://www.mdpi.com/1996-1073/10/4/556SF6negative DC-PDdecomposed componentsconcentration ratioback propagation neural networkPD recognition |
| spellingShingle | Ju Tang Xu Yang Gaoxiang Ye Qiang Yao Yulong Miao Fuping Zeng Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions Energies SF6 negative DC-PD decomposed components concentration ratio back propagation neural network PD recognition |
| title | Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions |
| title_full | Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions |
| title_fullStr | Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions |
| title_full_unstemmed | Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions |
| title_short | Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions |
| title_sort | decomposition characteristics of sf6 and partial discharge recognition under negative dc conditions |
| topic | SF6 negative DC-PD decomposed components concentration ratio back propagation neural network PD recognition |
| url | http://www.mdpi.com/1996-1073/10/4/556 |
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