Application of neural networks in numerical busbar protection systems (NBPS)

Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI:10.1109/ANN.1991.213508

K. Feser, U. Braun, F. Engler, A. Maier

引用次数: 11

Abstract

During the development of a (conventional) busbar protection algorithm which is able to cope with current signals distorted by current transducer saturation, the question came up, whether it would be possible to use a neural network for preprocessing the data and restoring the distorted signals. A training tool for neural networks and a set of typical distorted and undistorted current signals was selected for a verification of the idea. The test showed that the application of a neural network to this issue is possible in principal and that the signal quality is improved with respect to the needs of a busbar protection system, respectively. The ability of the neural networks to map an increasing number of input signals to reasonable output signals is investigated. Furthermore some studies were made for implementing the trained neural network in hardware.<>

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神经网络在数值母线保护系统中的应用

传统的母线保护算法在开发过程中，遇到了一个问题，即能否利用神经网络对数据进行预处理并恢复失真信号。选择神经网络训练工具和一组典型的失真和未失真电流信号进行验证。实验结果表明，将神经网络应用于该问题在原则上是可行的，信号质量相对于母线保护系统的要求有所提高。研究了神经网络将越来越多的输入信号映射到合理的输出信号的能力。在此基础上，对神经网络的硬件实现进行了研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the First International Forum on Applications of Neural Networks to Power Systems

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