基于BP神经网络算法的锁相环控制

Wenjin Dai, Youhui Xie, Hua Yang
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引用次数: 3

摘要

为了使电网并联运行,需要控制电流与电网电压同相。提出了一种基于人工神经网络的相位跟踪控制方法。该算法将BP网络算法转化为锁相环(PLL),以网络电压作为期望输出,电流作为训练样本。然后通过神经网络的自学习,逐渐减小样本与预期目标之间的输出误差,实现对预期输出的同步和跟踪。本文利用MATLAB仿真电源系统工具箱对其进行了数字动态仿真。结果表明,该方法能很好地跟踪目标,具有较强的自适应能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A PLL control based on algorithm of BP neural network
For the parallel operation of the electric power network, it needs to control the current to be the same phase with the electric power network voltage. This paper presents a control method of the phase tracking based on the artificial neural network. It takes the algorithm of BP network into Phase Locked Loop (PLL) and the electric network voltage as the expected output and current as the training sample. Then with the self-learning of neural network, it can gradually reduce the output error between the sample and the expected target and achieve the synchronization and tracking of the expected output. In this paper, it has been carried out through the digital dynamic simulation with the MATLAB Simulation Power System Toolbox. Its result shows it can track its target well and have a strong adaptive capacity.
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