Wuxi Guangying Group Co., Ltd., Wuxi 214000, Jiangsu, China.
Comput Intell Neurosci. 2022 Aug 31;2022:7623215. doi: 10.1155/2022/7623215. eCollection 2022.
In the practice of power line engineering, navigation and positioning technology is often used in the fields of information collection and analysis, optimized line design, and deformation monitoring. Compared with traditional measurement technology, it has the characteristics of high precision and high reliability. In order to realize the measurement of abnormal displacement of power lines, improve the efficiency and quality of monitoring, and reduce the occurrence of faults, firstly, this study introduces the basic theory of artificial neural network (ANN). The core algorithm of the ANN-BP (back propagation) neural network has been improved. The improved algorithm is used to improve the BeiDou Navigation Satellite System (BDS). The improved and the unimproved BDS are used to solve the collected related data. The results show that the geometric dilution of precision (GDOP) values obtained by conventional BDS are small, all within the range of less than 4. After the introduction of the BP neural network into the system, the geometric space distribution of positioning satellites is improved, the GDOP is reduced, the reliability and availability of satellite positioning are enhanced, and the accuracy requirements are met. The accuracy of the measured data positioning results of the two systems has reached the cm level. There is not much difference between the processing results of the two modes. Among them, the direction accuracy has the largest difference, which is 2.5 cm. The introduction of the BP neural network has improved the spatial combination structure, and the positioning results in the three directions of , , and are all better. From the perspective of root mean square (RMS), the RMS fluctuation of the simulation results obtained by observing the conventional BDS is large. The RMS value of BDS displacement based on the BP neural network is smaller, and the change is gentle. With the increase in the number of epochs and the increase in the number of simulations, its value is also more convergent. These data show that the quality of BDS observations based on the BP neural network is significantly better. These contents will effectively improve the monitoring accuracy and operational reliability of the system and have important practical significance and application value.
在电力线工程实践中，导航定位技术常用于信息采集与分析、优化线路设计、变形监测等领域。与传统测量技术相比，它具有高精度、高可靠性的特点。为了实现对电力线异常位移的测量，提高监测的效率和质量，降低故障的发生，本研究首先介绍了人工神经网络（ANN）的基本理论。改进了 ANN-BP（反向传播）神经网络的核心算法，利用改进算法对北斗导航卫星系统（BDS）进行了改进，利用改进后的和未改进的 BDS 对采集到的相关数据进行了解算。结果表明，常规 BDS 得到的几何精度稀释因子（GDOP）值较小，均小于 4。将 BP 神经网络引入系统后，改善了定位卫星的几何空间分布，降低了 GDOP，提高了卫星定位的可靠性和可用性，满足了精度要求。两种系统测量数据定位结果的精度均达到了厘米级。两种模式的处理结果差异不大，其中方向精度的差异最大，为 2.5cm。BP 神经网络的引入改善了空间组合结构， 、 、 三个方向的定位结果均有所提高。从均方根（RMS）的角度来看，常规 BDS 观测得到的模拟结果的 RMS 波动较大。基于 BP 神经网络的 BDS 位移的 RMS 值较小，变化较平缓。随着观测历元数的增加和模拟次数的增加，其值也更加收敛。这些数据表明，基于 BP 神经网络的 BDS 观测质量有了显著提高。这些内容将有效提高系统的监测精度和运行可靠性，具有重要的现实意义和应用价值。
