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基于混合熵筛选和改进独立分量分析方法的多谐波源系统谐波贡献评估

Evaluation of Harmonic Contributions for Multi Harmonic Sources System Based on Mixed Entropy Screening and an Improved Independent Component Analysis Method.

作者信息

Zhao Jinshuai, Yang Honggeng, Ma Xiaoyang, Xu Fangwei

机构信息

The College of Electrical Engineering, Sichuan University, Chengdu 610065, China.

出版信息

Entropy (Basel). 2020 Mar 12;22(3):323. doi: 10.3390/e22030323.

Abstract

Evaluating the harmonic contributions of each nonlinear customer is important for harmonic mitigation in a power system with diverse and complex harmonic sources. The existing evaluation methods have two shortcomings: (1) the calculation accuracy is easily affected by background harmonics fluctuation; and (2) they rely on Global Positioning System (GPS) measurements, which is not economic when widely applied. In this paper, based on the properties of asynchronous measurements, we propose a model for evaluating harmonic contributions without GPS technology. In addition, based on the Gaussianity of the measured harmonic data, a mixed entropy screening mechanism is proposed to assess the fluctuation degree of the background harmonics for each data segment. Only the segments with relatively stable background harmonics are chosen for calculation, which reduces the impacts of the background harmonics in a certain degree. Additionally, complex independent component analysis, as a potential method to this field, is improved in this paper. During the calculation process, the sparseness of the mixed matrix in this method is used to reduce the optimization dimension and enhance the evaluation accuracy. The validity and the effectiveness of the proposed methods are verified through simulations and field case studies.

摘要

评估每个非线性用户的谐波贡献对于具有多样且复杂谐波源的电力系统中的谐波抑制至关重要。现有的评估方法存在两个缺点:(1)计算精度容易受到背景谐波波动的影响;(2)它们依赖全球定位系统(GPS)测量,广泛应用时不经济。本文基于异步测量的特性,提出了一种无需GPS技术的谐波贡献评估模型。此外,基于实测谐波数据的高斯性,提出了一种混合熵筛选机制来评估每个数据段背景谐波的波动程度。仅选择背景谐波相对稳定的段进行计算,这在一定程度上降低了背景谐波的影响。此外,本文对作为该领域一种潜在方法的复独立分量分析进行了改进。在计算过程中,利用该方法中混合矩阵的稀疏性来降低优化维度并提高评估精度。通过仿真和现场案例研究验证了所提方法的有效性和实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ca7/7516780/38eac4862ad9/entropy-22-00323-g001.jpg

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