基于图像处理和人工神经网络的输电线路故障定位
Determination of Fault Location in Transmission Lines with Image Processing and Artificial Neural Networks
摘要：
为了以连续和高质量的方式传输电能，有必要从生产点到消费点对其进行控制。因此，在从生产到消费的每个阶段，输电和配电线路的保护都是必不可少的。在系统发生短路时，应尽快停用电气装置中保护继电器的主要功能。该系统最重要的部分是能量传输线路和保护这些线路的距离保护继电器。准确的误差定位技术是快速有效工作的前提。Transformer中性点接地影响电力系统单相接地故障时零分量电流的运行。考虑到接地系统和保护系统之间的关系，应做出适当的接地选择。人工神经网络（ANN）已被用于准确定位输电线路不同接地系统的短路故障。在PSCAD-EMTDC仿真程序中建立了输电线路模型。通过记录在不同接地系统中产生的短路故障中距离保护继电器的R-X阻抗图的阻抗变化的图像来创建数据集。利用特征提取和图像处理技术，对图像中的相关焦点进行了介绍，并选择了故障位置估计精度最高的神经网络模型。
英文摘要：
In order to transmit electrical energy in a continuous and quality manner, it is necessary to control it from the point of production to the point of consumption. Therefore, protection of transmission and distribution lines is essential at every stage from production to consumption. The main function of the protection relays in electrical installations should be deactivated as soon as possible in the event of short circuits in the system. The most important part of the system is energy transmission lines and distance protection relays that protect these lines. An accurate error location technique is required to make fast and efficient work. Transformer neutral point grounding in transmission lines affects the operation of the zero component current during the single phase to ground short circuit failure of a power system. Considering the relationship between the grounding system and protection systems, an appropriate grounding choice should be made. Artificial neural network (ANN) has been used in order to accurately locate short circuit faults in different grounding systems in transmission lines. Compared with support vector machines (SVM) for testing inside ANN The transmission line model is made in the PSCAD-EMTDC simulation program. Data sets were created by recording the image of the impedance change of the R-X impedance diagram of the distance protection relay in short circuit faults created in different grounding systems. The related focal points in the images are given as an introduction to different ANN models using feature extraction and image processing techniques and the ANN model with the highest fault location estimation accuracy was chosen.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
