基于复变自适应神经网络的电网相位估计方法
发布时间：2019-01-06 13:35
【摘要】：针对非理想电网电压下,不平衡电压、频率偏移引起的电网相位难以检测的问题,提出了一种在复变域下使用的基于自适应神经网络的电网相位估计方法.首先,对非理想电网电压进行建模,在得到神经网络模型的基础上,将复变最小均方算法的权值更新方法应用到神经网络权值更新过程中,利用神经网络权值实现对相位的估计.为了跟踪电网频率,设计了电网频率跟踪环节,并对收敛性进行了分析.仿真和实验的结果表明所提出的方法能够快速准确地对非理想电压下的电网相位进行估计.
[Abstract]:In order to solve the problem that the phase of the power system is difficult to detect due to the unbalanced voltage and frequency offset under the non-ideal network voltage, an adaptive neural network based power network phase estimation method is proposed in this paper. First of all, modeling the non-ideal network voltage, on the basis of obtaining the neural network model, applying the weight updating method of the complex minimum mean square algorithm to the neural network weight updating process. The phase estimation is realized by using neural network weights. In order to track the frequency of the power system, the frequency tracking link is designed and the convergence is analyzed. The simulation and experimental results show that the proposed method can estimate the phase of the power network quickly and accurately.
【作者单位】： 东北大学信息科学与工程学院;
【基金】：国家自然科学基金重点资助项目(61433004);国家自然科学基金资助项目(51467017)
【分类号】：TP183;TM711
本文编号：2402860
[Abstract]:In order to solve the problem that the phase of the power system is difficult to detect due to the unbalanced voltage and frequency offset under the non-ideal network voltage, an adaptive neural network based power network phase estimation method is proposed in this paper. First of all, modeling the non-ideal network voltage, on the basis of obtaining the neural network model, applying the weight updating method of the complex minimum mean square algorithm to the neural network weight updating process. The phase estimation is realized by using neural network weights. In order to track the frequency of the power system, the frequency tracking link is designed and the convergence is analyzed. The simulation and experimental results show that the proposed method can estimate the phase of the power network quickly and accurately.
【作者单位】： 东北大学信息科学与工程学院;
【基金】：国家自然科学基金重点资助项目(61433004);国家自然科学基金资助项目(51467017)
【分类号】：TP183;TM711
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