A distributed temperature and strain measurement method for OPPC in distribution Internet of Things in electricity based on multilayer feedforward artificial neural network

In order to effectively measure temperature and strain along opticalphase conductor (OPPC), the multilayer feedforward artificial neural network (ANN) is applied to demodulate the temperature and strain along OPPC composite optical fiber. The basic principle and parameters of ANN for this purpose ar...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-02, Vol.2187 (1), p.12068
Main Authors: Guan, Guofei, Song, Qingwu
Format: Article
Language:English
Subjects:
Artificial neural networks
Conductors
Internet of Things
Measurement methods
Multilayers
Neural networks
Optical fibers
Physics
Signal to noise ratio
Strain measurement
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Summary:In order to effectively measure temperature and strain along opticalphase conductor (OPPC), the multilayer feedforward artificial neural network (ANN) is applied to demodulate the temperature and strain along OPPC composite optical fiber. The basic principle and parameters of ANN for this purpose are introduced. ANN is trained by using the numerically generated Brillouin spectra with different values of signal-to-noise ratio (SNR) and Brillouin frequency shifts (BFS), and the training results are presented. The Brillouin spectra with different values of SNR, temperature and strain along the optical fiber are numerically generated. The temperature and strain along the optical fiber are demodulated by the spectrum fitting method and the ANN method. The results reveal that the multilayer feedforward artificial neural network method has similar accuracy with the spectrum fitting method. However, the computation time of the former is much less than that of the latter.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2187/1/012068