选择人工神经网络结构查找传输故障

A. Mazón, I. Zamora, J. Gracia, K. J. Sagastabeutia, J. Sáenz
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引用次数: 47

摘要

基于人工神经网络(ANN)的编程模型的使用越来越多。人工神经网络应用于各个领域(工业、医药、金融或新技术等)。神经网络在电力系统运行和控制过程中有广泛的应用前景,包括稳定性评估、安全监测、负荷预测、状态估计、负荷潮流分析、应急分析、应急控制动作、高压直流系统设计等。本文的特点是一个自动系统,它选择最适当的人工神经网络结构来解决任何类型的问题。为了获得对双端传输线故障定位效果较好的神经网络结构,将神经网络自动选择系统(SARENEUR)应用于具体实例。根据一端测得的稳态电压和电流值确定故障位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Selecting ANN structures to find transmission faults
Programming models based on artificial neural networks (ANN) have seen increased usage. ANNs are used in various fields (industry, medicine, finance, or new technology, among others). There is a wide range of possible power system applications of neural networks in operation and control processes, including stability assessment, security monitoring, load forecasting, state estimation, load flow analysis, contingency analysis, emergency control actions, HVDC system design, etc. This article features an automatic system that selects the most adequate ANN structure to solve any type of problem. The ANN Automatic Selection System (SARENEUR) was implemented in a specific case in order to obtain a neural network structure that shows better results in fault location within a two-terminal transmission line. The fault location is obtained according to the values of steady-state voltages and currents measured at one end.
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