Simultaneous and Dynamic Detection of SF6 Decomposition Products under Partial Discharge Defect of Gas-insulated Power Equipment by Fiber-Enhanced Raman Spectroscopy

Weiping Kong,Qiang Liu,Qiuchi Li,Xinrong Yao,Fu Wan

IEEE Transactions on Dielectrics and Electrical Insulation（2023）

Cited 2|Views19

Abstract

SF 6 decomposition product analysis can effectively obtain the health status of gas-insulated power equipment (GIPE). In this paper, to solve the drawbacks of aging, cross-interference, and time-sharing detection analysis of common gas detection technologies, a fiber-enhanced Raman spectroscopy (FERS) sensing system is used to realize the simultaneous and dynamic measurement of multiple SF 6 decomposition products. A hybrid collaborative fluorescence filtering method is proposed to simply and effectively improve the sensitivity of FERS by 2.7 times. The characteristic peaks for simultaneous quantitative and qualitative determination of SF 6 decomposition products are determined (811 cm -1 for SOF 2 , 851 cm -1 for SO 2 F 2 , 861 cm -1 for COS, 913 cm -1 for CF 4 , 1156 cm -1 for SO 2 , 1395 cm -1 for CO 2 and 2150 cm -1 for CO), and the corresponding simultaneous detection limit can be obtained as: 5.95 ppm·bar for SOF 2 , 3.75 ppm·bar for SO 2 F 2 , 1.89 ppm·bar for COS, 5.82 ppm·bar for CF 4 , 2.23 ppm·bar for SO 2 , 6.19 ppm·bar for CO 2 and 14.82 ppm·bar for CO with the laser power of 200 mW and the exposure time of 60 s. Finally, the dynamic analysis of SF 6 decomposition gases produced by metal protrusion partial discharge defect model is also achieved. The designed FERS sensing system fully demonstrates its ability to simultaneously and dynamically analyze the SF 6 decomposition products, which lays the foundation for more accurate and earlier diagnosis of SF 6 GIPE failures.

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Key words

Fiber-enhanced Raman spectroscopy (FERS),fluorescence filtering method,gas-insulated power equipment (GIPE),partial discharge (PD) defect,SF6 decomposition products

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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