A Quantification Method for Supraharmonic Emissions Based on Outlier Detection Algorithms

Hui Zhou, Zesen Gui, Jiang Zhang, Qun Zhou, Xueshan Liu and Xiaoyang Ma
Additional contact information
Hui Zhou: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Zesen Gui: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Jiang Zhang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Qun Zhou: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xueshan Liu: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xiaoyang Ma: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2021, vol. 14, issue 19, 1-18

Abstract: Based on outlier detection algorithms, a feasible quantification method for supraharmonic emission signals is presented. It is designed to tackle the requirements of high-resolution and low data volume simultaneously in the frequency domain. The proposed method was developed from the skewed distribution data model and the self-tuning parameters of density-based spatial clustering of applications with noise (DBSCAN) algorithm. Specifically, the data distribution of the supraharmonic band was analyzed first by the Jarque–Bera test. The threshold was determined based on the distribution model to filter out noise. Subsequently, the DBSCAN clustering algorithm parameters were adjusted automatically, according to the k -dist curve slope variation and the dichotomy parameter seeking algorithm, followed by the clustering. The supraharmonic emission points were analyzed as outliers. Finally, simulated and experimental data were applied to verify the effectiveness of the proposed method. On the basis of the detection results, a spectrum with the same resolution as the original spectrum was obtained. The amount of data declined by more than three orders of magnitude compared to the original spectrum. The presented method will benefit the analysis of quantification for the amplitude and frequency of supraharmonic emissions.

Keywords: supraharmonic; outlier detection; data distribution; DBSCAN; clustering algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/19/6404/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/19/6404/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:19:p:6404-:d:651097

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().