Classification of Multiple Power Quality Disturbances by Tunable-Q Wavelet Transform with Parameter Selection

Lin Yang, Linming Guo, Wenhai Zhang and Xiaomei Yang
Additional contact information
Lin Yang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Linming Guo: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Wenhai Zhang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xiaomei Yang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2022, vol. 15, issue 9, 1-16

Abstract: Identifying power quality (PQ) disturbances is an important prerequisite for developing mitigation measures to improve PQ. However, the coupling of multiple PQ disturbances in the noise condition makes it difficult to achieve effective feature extraction and classification. This article proposes a novel method to identify multiple PQ disturbances by integrating improved TQWT with XGBoost algorithm. The improved TQWT is proposed to automatically select the proper tuning parameters by screening the spectral information of PQ signals. Then, the improved TQWT is used to decompose PQ disturbances into sub-bands for further feature extraction. Optimum feature selection and classification are implemented in XGBoost. Classification accuracies of 26 categories of synthetic PQ disturbances under different noisy levels are tested and compared with existing methods. Results indicate that the proposed method is efficient and noise-resistant, and the classification accuracy can reach 97.63% under 20 dB noise, and keep above 99% under lower level noise.

Keywords: tunable-Q wavelet transform; tuning parameters; power quality disturbance; XGBoost classification (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/9/3428/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/9/3428/ (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:15:y:2022:i:9:p:3428-:d:810621

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 ().