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Application of soft computing neural network tools to line congestion study of electrical power systems

机译:软计算神经网络工具在电力系统中拥塞研究的应用

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摘要

This paper presents a scheme for application of soft computing neural network tools namely feed forward neural network with backpropagation, and radial basis function neural network for the study of transmission line congestion in electrical power systems. The authors performed sequential training of the two proposed neural networks for monitoring the level of line congestion in the system. Finally, a comparative analysis is drawn between the two neural networks and it is observed that radial basis function neural network yields fastest convergence. The proposed method is tested on the IEEE 30-bus test system subject to various operating conditions.
机译:本文介绍了软计算神经网络工具的应用方案,即馈送前向神经网络,并辐射基础函数神经网络,用于研究电力系统中的传输线拥塞。 作者对两个提议的神经网络进行了顺序训练,用于监测系统中的线路拥塞水平。 最后,在两个神经网络之间绘制比较分析,观察到径向基函数神经网络产生最快的收敛性。 在经过各种操作条件的情况下,在IEEE 30-Bus测试系统上测试所提出的方法。

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