Fiber optic current sensor temperature compensation through RBF neural network

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.

[1]  Jian-hua Cheng,et al.  Modification of an RBF ANN-Based Temperature Compensation Model of Interferometric Fiber Optical Gyroscopes , 2015, Sensors.

[2]  Jun Liu,et al.  Error characteristic analysis and experimental research on a fiber optic current transformer. , 2018, Applied optics.

[3]  Li Dexin,et al.  Fault diagnosis algorithm of fiber current transformer based on Wavelet-Allan variance , 2019 .

[4]  Chenguo Yao,et al.  Optical fiber sensor system for monitoring leakage current of post insulators based on RBF neural network , 2014, 2014 ICHVE International Conference on High Voltage Engineering and Application.

[5]  K. Bohnert,et al.  Temperature Compensation of Interferometric and Polarimetric Fiber-Optic Current Sensors With Spun Highly Birefringent Fiber , 2019, Journal of Lightwave Technology.

[6]  S. Ziegler,et al.  Current Sensing Techniques: A Review , 2009, IEEE Sensors Journal.

[7]  Leonardo S. Encinas,et al.  Improving optical fiber current sensor accuracy using artificial neural networks to compensate temperature and minor non-ideal effects , 2011, International Conference on Optical Fibre Sensors.

[8]  S Kucuksari,et al.  Experimental Comparison of Conventional and Optical Current Transformers , 2010, IEEE Transactions on Power Delivery.

[9]  Kiyoshi Kurosawa,et al.  Development of fiber-optic current sensing technique and its applications in electric power systems , 2013, Photonic Sensors.

[10]  Wei Li,et al.  Optical fiber current sensor research: review and outlook , 2016 .

[11]  Mohammad Hamed Samimi,et al.  Optical Current Transducers and Error Sources in Them: A Review , 2015, IEEE Sensors Journal.

[12]  Jagannathan Sarangapani,et al.  Neural Network Control of Nonlinear Discrete-Time Systems , 2018 .

[13]  Huiliang Cao,et al.  Temperature Energy Influence Compensation for MEMS Vibration Gyroscope Based on RBF NN-GA-KF Method , 2018, Shock and Vibration.

[14]  Kun Wang,et al.  Improved temperature stability of a fiber Sagnac-like detection system for atomic magnetometers. , 2020, Optics express.

[15]  Wan Gang,et al.  Quality Test and Problem Analysis of Electronic Transformer , 2012 .

[16]  Yang Yuan-hui.  Research of Temperature Compensation of Optical Fiber Current Sensor Based on Matlab , 2009 .

[17]  Yanjun Zhang,et al.  Hybrid algorithm combining genetic algorithm with back propagation neural network for extracting the characteristics of multi-peak Brillouin scattering spectrum , 2017 .