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A hybrid Adaline and Bacterial Foraging approach to power system harmonics estimation

机译:电力系统谐波估计的杂交糖苷和细菌觅食方法

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Harmonics estimation for a signal distorted with additive noise is an interdisciplinary area of interest for many researchers. This paper presents Bacterial Foraging Optimization (BFO) for estimating the fundamental as well as harmonic components present in power system voltage waveforms. The basic foraging strategy is made more adaptive by updating the weights of Adaptive Linear Neural Networks (Adaline) on taking the initial weighs as output of BFO. Performance of this new algorithm is compared with that of existing BFO.
机译:随着添加剂噪声失真的信号的谐波估计是许多研究人员的跨学科领域。本文介绍了用于估计电力系统电压波形中存在的基本和谐波分量的细菌觅食优化(BFO)。基本的觅食策略通过更新自适应线性神经网络(Adaline)作为BFO的输出而更新自适应线性神经网络(Adaline)的权重进行了更加自适应。将这种新算法的性能与现有的BFO的性能进行了比较。

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