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Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy

机译:基于稀疏分量分析和最小条件熵的多谐波源定位

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摘要

Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm.
机译:针对独立分量分析算法需要更多的测量点并且无法在未定条件下解决谐波源位置的问题,基于稀疏分量分析和用于识别分配系统中多次谐波源位置的最小条件熵的新方法建议的。在网络阻抗未知的情况下,谐波源的数量未确定,测量节点配置算法选择节点位置以使分离的谐波电流更准确。然后,使用所选节点的谐波电压数据作为输入,使用稀疏分量分析来解决未定条件下的谐波电流波形。最后,计算谐波电流和系统节点之间的条件熵,并且对应于最小条件熵的节点是谐波源的位置。为了验证所提出的方法的有效性和准确性,在IEEE 14节点系统中执行模拟。此外,与独立分量分析算法的结果相比。仿真结果验证了所提出的算法的正确性和有效性。

著录项

  • 期刊名称 Entropy
  • 作者单位
  • 年(卷),期 2020(22),1
  • 年度 2020
  • 页码 65
  • 总页数 14
  • 原文格式 PDF
  • 正文语种
  • 中图分类
  • 关键词

    机译:稀疏分量分析;条件熵;网络阻抗;独立分量分析;谐波源定位;

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