摘要
针对目前单纯采用点式覆冰监测终端对输电线路覆冰情况实时监测时,因终端续航时间短、摄像球机本身凝冰或结雾、拉力传感器难校准、终端安装维护困难、覆盖范围有限等造成覆冰效能低等问题,提出了一种基于神经网络与布里渊散射监测技术的线路覆冰计算方法。通过在OPGW线路两端安装布里渊传感器,测量布里渊散射信号的强度及频移,计算任意点光纤温度及应变,利用RBF神经网络对应变及气象参数建模,计算线路覆冰厚度。经系统运行数据证明,该方法能够有效结合气象参数修正OPGW应力法计算的覆冰厚度误差,提高覆冰监测效率及准确性。
According to problems in the online monitor of transmission line icing by using only point monitor terminals, such as short lift time of terminal, icing or fogging of dome camera, difficult calibration of force sensor, difficult installation and maintenance of terminal, and limited covering range, and so on, a calculation method of icing line based on neural network and Brillouin scattering monitoring technology is proposed, by the installation of Brillouin sensors at both ends of OPGW line, the strength and frequency shift of Brillouin scattering signals can be measured, which is used to calculate fiber temperature and strain. Strain and meteorological parameters are modeled based on RBF neural network to calculate the thickness of line icing. It is proved by system operation data that the proposed method, which effectively combines meteorological parameters, can correct the error of icing thickness calculated by the OPGW stress method and thus can improve the efficiency and accuracy of the line icing monitor.
出处
《广西电力》
2012年第5期10-14,共5页
Guangxi Electric Power
