Fiber optic current sensor temperature compensation through RBF neural network

Chen Liu; Ding Wang; Chenggang Li; Kuo Su; Dexin Li; Dafei Yu; Liqing Wang; Lei Si; Junjie Jin

doi:10.1117/12.2579466

5 November 2020 Fiber optic current sensor temperature compensation through RBF neural network

Chen Liu, Ding Wang, Chenggang Li, Kuo Su, Dexin Li, Dafei Yu, Liqing Wang, Lei Si, Junjie Jin

Proceedings Volume 11569, AOPC 2020: Optical Information and Network; 1156908 (2020) https://doi.org/10.1117/12.2579466
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

The fiber optic current sensor (FOCS) is susceptible to external temperature in actual operation, which will lead to its accuracy deviation, even malfunction. In order to improve the temperature stability of FOCS’s ratio error, a temperature compensation method based on RBF neural network is established by taking the temperature as input and the ratio error as output to the network. Compared with BP neural network, the simulation results show that the temperature compensation model based on RBF neural network has better accuracy whose prediction error is less than 3%. At the same time, the experimental results show that the drift deviation of ratio error can remain as low as ±0.1% in the range of -40 °C to 70°C, and the 0.2S-level accuracy of GBT20840.8 standard can be achieved.

Citation Download Citation

Chen Liu, Ding Wang, Chenggang Li, Kuo Su, Dexin Li, Dafei Yu, Liqing Wang, Lei Si, and Junjie Jin "Fiber optic current sensor temperature compensation through RBF neural network", Proc. SPIE 11569, AOPC 2020: Optical Information and Network, 1156908 (5 November 2020); https://doi.org/10.1117/12.2579466

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