递归神经网络电压控制器

Y. Kojima, Y. Izui, S. Kyomoto, T. Goda
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引用次数: 0

摘要

电力系统需要电压和无功控制(VQ)来避免电压崩溃。然而,传统的VQ控制由于其近似控制而不能满足这一要求。提出了一种利用具有系统动力学处理能力的递归神经网络进行VQ控制的新算法。首先,提出了被控目标的动力学和逆动力学学习算法。其次,将该算法应用于VQ控制。作者称这种控制器为“神经VQC”。最后,通过与传统VQ控制器的比较,证明了神经VQC的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Voltage controller with recurrent neural networks
An electric power system requires voltage and reactive power control (VQ control) to avoid voltage collapse. The conventional VQ control, however, does not meet this requirement because of approximated control. The authors propose a new algorithm for VQ control using recurrent neural networks which have the ability to treat system dynamics. Firstly, they propose the learning algorithm for dynamics and inverse dynamics of the controlled target. Secondly, they apply this algorithm to the VQ control. The authors call this controller 'neuro VQC'. Finally, the usefulness of the neuro VQC is shown in comparison with the conventional VQ controller.<>
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