Abstract

In modern microprocessor terminals for protecting traction networks, the measured circuit current is converted to a digital signal. There are various automatic techniques of recognizing and classifying digitalized analog signals. The technique that is attracting the greatest interest is the technique of neural networks (NNs), with its high flexibility in differentiating analyzed data. With a sufficient cluster set of learning samples that consist, for example, of operating and emergency current measurements, a NN applied to a traction network can be adjusted to classify the modes observed in the traction network. The integration of this function with the microprocessor terminal for protecting the traction network feeder allows reducing the number of false responses by adapting more accurately to the operational features of the traction power supply system. In the case of changes in the line’s operation conditions—for example, with a change in the type of electric rolling stock—the neural network can be retrained fairly quickly.

中文翻译：

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利用神经网络的质量能力确定牵引网络中的紧急模式

摘要

在用于保护牵引网络的现代微处理器终端中，测得的电路电流被转换为数字信号。有多种自动技术可以识别和分类数字化模拟信号。引起最大兴趣的技术是神经网络（NNs）技术，它在区分分析数据方面具有很高的灵活性。利用足够的群集样本集，例如由运行和紧急电流测量值组成的学习样本，可以调整应用于牵引网络的神经网络，以对在牵引网络中观察到的模式进行分类。该功能与用于保护牵引网络馈线的微处理器终端的集成，可以通过更准确地适应牵引电源系统的运行特征来减少错误响应的次数。在生产线运行条件发生变化的情况下（例如，随着电气机车车辆类型的变化），可以相当快地重新训练神经网络。