Novelty method for the utility harmonic impedance estimation based on Gaussian mixed model

被引:9
作者
Tang, Zhirong [1 ]
Shu, Qin [2 ]
Xu, Fangwei [2 ]
Jiang, Yue [2 ]
机构
[1] Chengdu Univ Technol, Coll Nucl Technol & Automat Engn, Chengdu 610059, Peoples R China
[2] Sichuan Univ, Coll Elect Engn, Chengdu 610065, Peoples R China
基金
中国国家自然科学基金;
关键词
expectation-maximisation algorithm; Gaussian processes; equivalent circuits; harmonic analysis; iterative methods; electric impedance; Gaussian distribution; power system harmonics; statistical distributions; parameter estimation; probability; novelty method; utility harmonic impedance estimation; Gaussian mixed model; PCC; harmonic control; harmonic emission level; harmonic responsibility analysis; novel estimation method; GMM; Norton equivalent circuit model; expectation maximisation iterative method; existing estimation methods; background harmonics;
D O I
10.1049/iet-gtd.2019.1349
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The accurate estimation of harmonic impedance on the utility side of a point of common coupling (PCC) is much important to harmonic control, harmonic emission level, and harmonic responsibility analysis. In this study, a novel estimation method for the utility harmonic impedance is proposed, which employs the Gaussian mixed model (GMM) to fit the measured data at a PCC, because the authors find that the statistical distribution of the measured data at a PCC is close to the Gaussian distribution. Also, in theory, GMM can approach the probability distribution of any data. In this method, the Norton equivalent circuit model at a PCC is first expressed as a GMM, and the utility harmonic impedance in the GMM is estimated by expectation maximisation iterative method. Compared to the existing estimation methods, an outstanding advantage of this method is that it is less affected by background harmonics; therefore, higher accuracy can be acquired in this method. The effectiveness and accuracy of this method are verified by simulation experiments and field data.
引用
收藏
页码:2573 / 2580
页数:8
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