Study on the mathematical model of multi-source time series prediction for gas insulated power equipment and the application of new environment-friendly insulating gas

Shiling Zhang; Liangjun Dai; Qiang Yao

doi:10.1117/12.2673741

22 May 2023 Study on the mathematical model of multi-source time series prediction for gas insulated power equipment and the application of new environment-friendly insulating gas

Shiling Zhang, Liangjun Dai, Qiang Yao

Author Affiliations +

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126401N (2023) https://doi.org/10.1117/12.2673741
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

In this paper, C5F10O (perfluorpentone, C5 for short) is decomposed in GIS/GIL due to the existence of insulation defects. C5F10O will produce typical decomposition gases such as CO, C3F6, CO2, C3F8, C2F6, C2F4 and CF4 under overheating conditions. The concentration of the above gases under the overheating conditions is a strong correlation series of time. It is necessary to carry out high-precision fitting for the time series obtained from the above tests. First, Gauss Chebyshev process is used to reconstruct the background value, which realizes high-precision fitting and prediction and early warning of two-dimensional nonlinear time series. At the same time, the maximum likelihood estimation of three-dimensional curved surface combined prediction model is realized. This paper first analyzes orthogonal prediction model based on the grey Markov chain Gauss Chebyshev from the mathematical model level, then analyzes the decomposition characteristics of C5F10O/CO2 under local overheating conditions and its prediction technology, and obtains the optimal ratio of new environment-friendly gas insulation, which has good guiding significance for the application of the new insulation gas to the operation, the maintenance and repair technology of the high-voltage electrical equipment.

Citation Download Citation

Shiling Zhang, Liangjun Dai, and Qiang Yao "Study on the mathematical model of multi-source time series prediction for gas insulated power equipment and the application of new environment-friendly insulating gas", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126401N (22 May 2023); https://doi.org/10.1117/12.2673741

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KEYWORDS

Geographic information systems

Dielectrics

3D modeling

Mathematical modeling

Metals

Gases

Carbon monoxide

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