基于人工神经网络的智能化架空线路应变快速解调方法

为了提高智能化光纤复合架空线路态势感知的实时性,将人工神经网络方法应用于光纤沿线应变解调,确定了神经网络的结构。编程实现了基于洛伦兹模型的最小二乘谱拟合方法和神经网络方法,采用不同信噪比和布里渊频移的布里渊谱训练神经网络,将它们应用于某光纤复合架空线路沿线光纤应变的测量,从不同角度比较了两种方法的计算结果。计算结果表明,神经网络方法能有效获得光纤沿线的布里渊频移进而获得应变,具有与谱拟合方法相似的准确性,但应变解调时间仅约为谱拟合方法的1/20000。研究结果为提高智能光纤复合架空线路态势感知的实时性提供了参考。

A Fast Strain Demodulation Method for Intelligent Overhead Line Based on Artificial Neural Network

In order to improve the real-time performance in situation awareness of intelligent optical fiber composite overhead line, the artificial neural network (ANN) method is introduced into the strain demodulation along the optical fiber. The structure of the ANN for strain demodulation is determined. The programs of the least-squares spectrum fitting method and ANN method based on Lorentzian model are written. The ANN is trained by the Brillouin spectra with different signal-to-noise ratios (SNRs) and Brillouin frequency shifts. The trained ANN is applied to the optical fiber strain measurement for an optical fiber composite overhead line. The results of the two methods are compared from different aspects. The results indicate that the ANN method can effectively obtain the Brillouin frequency shift along the optical fiber and then obtain the strain, which has the similar accuracy as the spectral fitting method. However, its computation time is much less than that of the spectrum fitting method. The work provides a reference value for improving the real-time performance of situation awareness of intelligent optical fiber composite overhead lines.