Power transformer condition assessment based on online monitor with SOFC chromatographic detector. (June 2020)

• Record Type: Journal Article
• Authors: Fan, Jingmin; Fu, Chenyang; Yin, Hao; Wang, Yu; Jiang, Qinji
• Is Part Of: International journal of electrical power & energy systems. Volume 118(2020)
• Publication Date: 2020-06
• Subjects: SOFC gas sensors -- Dissolved gas analysis (DGA) -- Power transformer -- Condition assessment -- Online monitor; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.105805
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: An online monitor based on SOFC gas sensor for DGA is designed and developed. The minimum detectability of the monitor to hydrocarbons lower than 0.1 ppm. The results of the online monitor can be calculated directly without calibration. Abstract: Power transformer is vital for energy conversion in power system, the condition assessment of which based on dissolved gas analysis (DGA) is used worldwide. In this paper, an online monitor based on solid oxide fuel cell (SOFC) detector is designed to address the poor stability, low sensitivity of other online-monitoring system. The system is composed of a gas measurement subsystem, a central controlling unit, and an intelligent fault diagnosis subsystem. A portable gas chromatography module developed using SOFC detector. The quantificational algorithm is derived, on basis of which calibration can be skipped. Due to the essential role of the chromatography column as well as gas the detector's temperature, proportional-integral-differential (PID) controller is implemented in the controlling unit. To evaluate the effectiveness of the developed online monitor, the quantitative analysis and engineering application have been performed; then, based on the measured results, the RVM-ANFIS algorithm is adopted to diagnosis the latent transformers faults. Using the developed online monitor, the results indicate that five characteristic gases, H2, CH4, C2 H4, C2 H6, and C2 H2 can be measured accurately, and incipient faults can be detected, validate the effectiveness of this system.