基于神经网络的降阶电力系统稳定器设计

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI:10.1109/CCECE.2017.7946634

M. Masrob, M. Rahman, G. George

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引用次数: 7

摘要

本文采用降阶电力系统实现了一种简单的人工神经网络电力系统稳定器(san - pss)。变压器的高母线电压的知识被用来改变Heffron-Phillips的方法。在此基础上，采用降约技术改变电力系统的顺序，并根据运行条件的变化实时调整控制器参数，提高电力系统的稳定性。仿真结果证实并提高了所提出的san - pss在变工况下电力系统的稳定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Design of a neural network based power system stabilizer in reduced order power system

In this paper, a simple artificial neural network power system stabilizer (SANN-PSS) is implemented using a reduced order power system. Knowledge of the high bus voltage of a transformer is employed to alter the Heffron-Phillips' method. Furthermore, a reduction technique is applied to change the power system's sequence, and increase the power system's stability by adjusting the controller's parameters in real time in response to modifications in the operating conditions. The simulation results confirm and enhance the power system's stability with the suggested SANN-PSS at altered operating conditions.

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来源期刊

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)

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