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首页> 外文期刊>Electric Power Components and Systems >Detection and Classification of Voltage Disturbances in Electrical Power Systems Using a Modified Euclidean ARTMAP Neural Network with Continuous Training
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Detection and Classification of Voltage Disturbances in Electrical Power Systems Using a Modified Euclidean ARTMAP Neural Network with Continuous Training

机译：使用改进的欧几里德式ARTMAP神经网络进行连续训练，对电力系统中的电压干扰进行检测和分类

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摘要

This article presents a method to detect and classify voltage disturbances in electric power distribution systems using a modified Euclidean ARTMAP neural network with continuous training. This decision-making tool accelerates the procedures to restore the normal operation conditions providing security, reliability, and profits to utilities. Furthermore, it allows the diagnosis system to adapt to changes from the constant evolution of the electric system. The voltage signals features or signatures are extracted using discrete wavelet transform, multiresolution analysis, and the energy concept. Results show that the proposed methodology is robust and efficient, providing a fast diagnosis process. The data set used to validate the proposal is obtained by simulations in a real distribution system using ATP software.
机译：本文提出了一种使用经过改进的欧几里德ARTMAP神经网络进行连续训练来检测和分类配电系统中电压干扰的方法。该决策工具可加快恢复正常运行条件的过程，从而为公用事业提供安全性，可靠性和利润。此外，它允许诊断系统适应电气系统不断发展的变化。使用离散小波变换，多分辨率分析和能量概念提取电压信号的特征或信号。结果表明，所提出的方法是鲁棒且有效的，提供了快速的诊断过程。用于验证提案的数据集是通过使用ATP软件在真实的配送系统中进行仿真获得的。

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