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Harmonic Source Localization Approach Based on Fast Kernel Entropy Optimization ICA and Minimum Conditional Entropy

机译:基于快速核熵优化ICA和最小条件熵的谐波源定位方法

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Based on the fast kernel entropy optimization independent component analysis and the minimum conditional entropy, this paper proposes a harmonic source localization method which aims at accurately estimating harmonic currents and identifying harmonic sources. The injected harmonic currents are estimated by the fast kernel entropy optimization independent component analysis (FKEO-ICA) in the absence of prior knowledge of harmonic impedances. Then, the minimum conditional entropy is applied to locate the harmonic sources based on the estimated harmonic currents. The proposed harmonic source localization method is validated on the IEEE 34-bus system. By applying the correlation coefficient and three error evaluation indicators, comparison has been made among the performances of the FKEO-ICA and three other ICA algorithms. The results show that the FKEO-ICA algorithm could achieve a significantly better accuracy of harmonic current estimation, while the minimum conditional entropy could determine the locations of harmonic sources precisely.
机译:在快速核熵优化独立分量分析和最小条件熵的基础上,提出了一种谐波源定位方法,旨在准确估计谐波电流并识别谐波源。在缺少谐波阻抗的先验知识的情况下,通过快速核熵优化独立分量分析(FKEO-ICA)估计注入的谐波电流。然后,基于估计的谐波电流,应用最小条件熵来定位谐波源。所提出的谐波源定位方法在IEEE 34总线系统上得到了验证。通过应用相关系数和三个误差评估指标,对FKEO-ICA和其他三个ICA算法的性能进行了比较。结果表明,FKEO-ICA算法可以明显提高谐波电流估计的准确性,而最小条件熵可以精确地确定谐波源的位置。

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