Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/209563
Title: An Online Monitoring System for Oil Immersed Power Transformer Based on SnO<sub>2</sub> GC Detector With a New Quantification Approach
Authors: Jingmin Fan;Feng Wang;Qiuqin Sun;Feng Bin;Huisheng Ye;Yuhan Liu
Year: 2017
Publisher: IEEE
Abstract: In this paper, an online monitoring system based on a SnO<sub>2</sub> gas chromatographic detector for power transformer condition assessment is developed. For a quantitative analysis of feature gases dissolved in transformer oil, a mathematical model derived from the chemistry and semiconductor theory is proposed. On the basis of this, gas chromatography measurement module along with electronic controlling and data sampling system is designed and integrated. A series of repeatability test and quantitative analysis has been performed; the repeatability performance is excellent for given concentrations; the measurement accuracy based on the proposed mathematical model for the feature gases shows good suitability. Furthermore, the transformer diagnosis is performed for identifying fault types. The experimental and practical application results demonstrate the effectiveness of this system.
URI: http://localhost/handle/Hannan/209563
volume: 17
issue: 20
More Information: 6662,
6671
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7997884.pdf1.41 MBAdobe PDF
Title: An Online Monitoring System for Oil Immersed Power Transformer Based on SnO<sub>2</sub> GC Detector With a New Quantification Approach
Authors: Jingmin Fan;Feng Wang;Qiuqin Sun;Feng Bin;Huisheng Ye;Yuhan Liu
Year: 2017
Publisher: IEEE
Abstract: In this paper, an online monitoring system based on a SnO<sub>2</sub> gas chromatographic detector for power transformer condition assessment is developed. For a quantitative analysis of feature gases dissolved in transformer oil, a mathematical model derived from the chemistry and semiconductor theory is proposed. On the basis of this, gas chromatography measurement module along with electronic controlling and data sampling system is designed and integrated. A series of repeatability test and quantitative analysis has been performed; the repeatability performance is excellent for given concentrations; the measurement accuracy based on the proposed mathematical model for the feature gases shows good suitability. Furthermore, the transformer diagnosis is performed for identifying fault types. The experimental and practical application results demonstrate the effectiveness of this system.
URI: http://localhost/handle/Hannan/209563
volume: 17
issue: 20
More Information: 6662,
6671
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7997884.pdf1.41 MBAdobe PDF
Title: An Online Monitoring System for Oil Immersed Power Transformer Based on SnO<sub>2</sub> GC Detector With a New Quantification Approach
Authors: Jingmin Fan;Feng Wang;Qiuqin Sun;Feng Bin;Huisheng Ye;Yuhan Liu
Year: 2017
Publisher: IEEE
Abstract: In this paper, an online monitoring system based on a SnO<sub>2</sub> gas chromatographic detector for power transformer condition assessment is developed. For a quantitative analysis of feature gases dissolved in transformer oil, a mathematical model derived from the chemistry and semiconductor theory is proposed. On the basis of this, gas chromatography measurement module along with electronic controlling and data sampling system is designed and integrated. A series of repeatability test and quantitative analysis has been performed; the repeatability performance is excellent for given concentrations; the measurement accuracy based on the proposed mathematical model for the feature gases shows good suitability. Furthermore, the transformer diagnosis is performed for identifying fault types. The experimental and practical application results demonstrate the effectiveness of this system.
URI: http://localhost/handle/Hannan/209563
volume: 17
issue: 20
More Information: 6662,
6671
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7997884.pdf1.41 MBAdobe PDF