Koordinasi Adaptif DOCR Pada Sistem Transmisi Loop Multi Generator Menggunakan Modified Firefly Algorithm-Artificial Neural Network

Yolanda Dewi Puspita Ayu, Daeng Rahmatullah, Istiyo Winarno
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Abstract

Protection system is a very important component in the electric power distribution system. With good reliability, the distribution of electrical power will be better, one of which is by minimizing disruption to the system quickly, precisely and accurately. Components used in protection systems are directional overcurrent relays (DOCR). In this study proposed optimal and adaptive protection coordination using the Modified Firefly Algorithm-Artificial Neural Network (MFA-ANN) tested on the IEEE 9 real bus loop system with 4 generation combinations. Optimization using MFA will get the Time Delay Setting (TDS) and Ipickup (lowset) values. The ANN used is Backpropagation Lavenberg Marquardt (BPLM) After the BPLM learning process, we will get the weight and bias values of the training results and later as a reference value to get the value of TDS and Ipickup relay. The process will produce relay settings automatically based on the results of optimization and MFA-ANN training which are then simulated on the IEEE 9 bus loop system. The results achieved by MFA-ANN are suitable methods for modeling optimal and adaptive relay coordination systems. ANN training with the BPLM algorithm produces the smallest MSE value of 5.9308xE-10.
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保护系统是配电系统中非常重要的组成部分。有了良好的可靠性,电力的分配将会更好,其中之一是通过快速,精确和准确地减少对系统的干扰。保护系统中使用的元件是定向过流继电器(DOCR)。本文提出了一种基于改进萤火虫算法-人工神经网络(MFA-ANN)的优化自适应保护协调方法,并在IEEE 9实际总线环路系统上进行了4代组合测试。使用MFA优化将获得时间延迟设置(TDS)和Ipickup (lowset)值。使用的神经网络是反向传播拉文伯格·马夸特(BPLM)。经过BPLM学习过程,我们将得到训练结果的权重和偏置值,然后作为参考值得到TDS和Ipickup继电器的值。该过程将根据优化和MFA-ANN训练的结果自动产生继电器设置,然后在ieee9总线环路系统上进行仿真。MFA-ANN的研究结果适用于最优和自适应继电器协调系统的建模。使用BPLM算法训练的ANN得到最小的MSE值为5.9308xE-10。
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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