Hopfield neural network-based estimation of harmonic currents in power systems

被引:0
|
作者
Wang, Ping [1 ]
Zou, Yu [1 ]
Zou, Shuangyi [1 ]
Sun, Yugeng [1 ]
机构
[1] Tianjin Univ, Sch Elect Engn & Automat, Tianjin 300072, Peoples R China
关键词
neural network; harmonic currents; estimation; power system;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A novel approach for adaptive estimation of instantaneous harmonic currents in the power system is proposed in this paper. The signal processing technique based on Hopfield neural network (HNN) optimum theory is applied for the detection of harmonic components generated by nonlinear current loads. Instead of training the network, the basic principle of neural network is applied to determine the magnitude and phase of the fundamental and each of the harmonic current components adaptively. The correct solution is obtained in real time. The computer simulation results show that the method has the characteristics of real time, high precision and adaptive tracing to load currents.
引用
收藏
页码:7494 / 7497
页数:4
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