基于人工神经网络的电力系统网络攻击保护

IF 0.6 4区 工程技术 Q4 Engineering
Md. Shahidul Islam, Ruet, Shafia Sultana, Md. Motakabbir Rahman
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引用次数: 1

摘要

本文讨论了网络攻击引起的频率和电压扰动对孤立电力系统的影响,提出了一种基于神经网络的保护方法。采用基于遗传算法的人工神经网络实现了负载频率控制和电压自动调节的自适应PID控制器。PID控制器的参数通过使用遗传算法在广泛的系统参数变化范围内离线调整。这些数据被用来训练神经网络。采用三输入开关控制调速器调速和放大器增益。对于负载频率控制,神经网络调谐PID控制器比手动调谐PID控制器减轻频率干扰的速度快48%;对于自动电压调节器,神经网络调谐PID控制器在网络攻击时减轻电压干扰的速度比手动调谐PID控制器快70%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protection of Power System during Cyber-Attack using Artificial Neural Network
Impacts of frequency and voltage disturbance on an isolated power system caused by cyber-attack have been discussed, and a neural network-based protective approach has been proposed in this research work. Adaptive PID controllers for both load frequency control and automatic voltage regulator have been implemented using an artificial neural network-oriented by genetic algorithm. The parameters of the PID controller have been tuned offline by using a genetic algorithm over a wide range of system parameter variations. These data have been used to train the neural network. Three input switch has been implemented to control governor speed regulation and amplifier gain. For load frequency control neural network tuned PID controller mitigate frequency disturbance 48% faster than manually tuned PID and for the automatic voltage regulator, neural network tuned PID controller mitigate voltage disturbance 70% faster than manually tuned PID during cyber-attack.
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来源期刊
Nuclear Engineering International
Nuclear Engineering International 工程技术-核科学技术
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Information not localized
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