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IEEE-519-based real-time and optimal control of active filters under nonsinusoidal line voltages using neural networks

机译:使用神经网络在非正弦线电压下基于IEEE-519的有源滤波器的实时和最佳控制

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摘要

In this paper, a fast and simple neural network (NN)-based control system for shunt active power filters operating under distorted voltage conditions is developed. The proposed system is an enhanced version of the optimal and flexible control (OFC) strategy with very fast and simple structure. In the proposed system, the time-consuming and complex nonlinear optimization algorithm required by OFC is replaced by a simple 3-layer perceptron NN. The NN is trained off-line using some random data based on the IEEE-519 Standard, while it can be used for a very wide range of new voltage waveforms in practice. The proposed system has been developed after introducing a new version of the OFC strategy in a-b-c frame of reference. This system satisfies both theoretical and practical requirements. Several simulation results using MATLAB toolboxes under highly distorted and unbalanced voltages have been provided to validate the ability of the proposed control system.
机译:在本文中,开发了一种基于快速和简单的神经网络(NN)的控制系统,用于在失真电压条件下工作的并联有源电力滤波器。所提出的系统是最优灵活控制(OFC)策略的增强版本,具有非常快速和简单的结构。在提出的系统中,OFC所需的耗时且复杂的非线性优化算法被简单的3层感知器NN取代。 NN是根据IEEE-519标准使用一些随机数据进行离线训练的,而实际上它可以用于非常广泛的新电压波形。在a-b-c参考框架中引入了新版本的OFC策略之后,便开发出了拟议的系统。该系统满足理论和实践要求。使用MATLAB工具箱在高度失真和不平衡的电压下提供了一些仿真结果,以验证所提出的控制系统的能力。

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