RBF神经网络在谐波检测中的应用
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摘要
有源电力滤波器补偿性能与所采用的谐波检测方式有很大的依赖关系,现有的检测方法存在精度不高、对电网频率变化比较敏感、自适应能力不强的缺点。本文提出基于RBF神经网络的谐波检测方法,具有较高的运算速度、较高的检测谐波精度,以及较强的自适应能力。
The compensating capability of active power filter has relation with the way of harmonic detection, but the existing methods of harmonic detection have some disadvantages,such as the lower harmonic precision, sensitive to the change of the power system frequency, lower self-adaptive capability. This paper put forward a method of harmonic detection based on the RBF neural networks which can engage precise detection with high speed and self-adaptive capability.
引文
[1]王群,吴宁,王兆安.一种基于人工神经网络的谐波检测方法[J].电网技术,1999,23(1):29-32.
    [2]王兆安,杨君,刘进军.谐波抑制和无功功率补偿[M].北京:机械工业出版社,2002.
    [3]ROBERT J,JAMES S,CARROLL J.Approximation of nonlinear systems with radial basis function neural networks[J].IEEE TAR-SLACTIONS ON NEURAL NETWORKS,2001,12(1):21-28.
    [4]王炜,吴耿锋,张博锋,等.径向基函数(RBF)神经网络及其应用[J].地震,2005,25(2):19-25.

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