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Design static VAR compensator controller using artificial neural network optimized by modify Grey Wolf Optimization

机译:利用改进的灰狼优化算法优化的人工神经网络设计静态无功补偿器控制器

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This paper introduce a novel design of the static VAR compensator (SVC) controller for damping power system oscillations. A multi layer neural network model tuned by Grey Wolf Optimization algorithm (GWO) is investigated and presented. GWO search algorithm is used to optimized all the connection of weights and biases for the artificial neural network. The proposed approach depends up on the expected wide range of the effective operating conditions of the SVC. Modification is introduced in the proposed optimizer exploration-exploitation balance to enhance its rate of convergence over the original algorithm. The robustness of the proposed controller successfully testing for damping oscillations of two-axis nonlinear single machine infinite bus system. A comparative study for the controller based the classical PI controller have been presented.
机译:本文介绍了一种用于抑制电力系统振荡的静态无功补偿器(SVC)控制器的新颖设计。研究并提出了一种基于灰狼优化算法(GWO)的多层神经网络模型。 GWO搜索算法用于优化人工神经网络的所有权重和偏差连接。提议的方法取决于SVC的有效运行条件的预期范围。在拟议的优化器勘探开发平衡中引入了修改,以提高其收敛速度。所提出的控制器的鲁棒性成功地测试了两轴非线性单机无限母线系统的阻尼振荡。对基于经典PI控制器的控制器进行了比较研究。

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