将偏最小二乘和前馈神经网络技术应用于静态安全评估问题的自动事件分组

Large Engineering Systems Conference on Power Engineering, 2003 Pub Date : 2003-05-07 DOI:10.1109/LESCPE.2003.1204684

D. Fischer, B. Szabados, S. Poehlman

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引用次数: 7

摘要

本文介绍了如何训练多个前馈-反向传播神经网络来预测突发事件后的电力系统母线电压。该方法旨在使用很少的学习示例。因此适合在线使用。将该方法应用于10机39母线的新英格兰电力系统模型。

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Automatic contingency grouping using partial least squares and feed forward neural network technologies applied to the static security assessment problem

The paper shows how a number of feed forward back propagation neural networks can be trained to predict power system bus voltages after a contingency. The approach is designed to use very few learning examples. thus being suitable for on-line use. The method was applied to the 10-machine, 39-bus New England Power System model.

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Large Engineering Systems Conference on Power Engineering, 2003

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