Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy Optimization

Hui Hwang Goh, Ling Liao, Dongdong Zhang, Wei Dai, Chee Shen Lim, Tonni Agustiono Kurniawan, Kai Chen Goh and Chin Leei Cham
Additional contact information
Hui Hwang Goh: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Ling Liao: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Dongdong Zhang: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Wei Dai: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Chee Shen Lim: Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou Industrial Park, 111 Ren’ai Road, Suzhou 215028, China
Tonni Agustiono Kurniawan: College of Environment and Ecology, Xiamen University, Xiamen 361102, China
Kai Chen Goh: Department of Technology Management, Faculty of Construction Management and Business, University Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia
Chin Leei Cham: Faculty of Engineering (FOE), BR4081, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia

Energies, 2022, vol. 15, issue 9, 1-21

Abstract: Noise significantly reduces the detection accuracy of transient power quality disturbances. It is critical to denoise the disturbance. The purpose of this research is to present an improved wavelet threshold denoising method and an adaptive parameter selection strategy based on energy optimization to address the issue of unclear parameter values in existing improved wavelet threshold methods. To begin, we introduce the peak-to-sum ratio and combine it with an adaptive correction factor to modify the general threshold. After calculating the energy of each layer of wavelet coefficient, the scale with the lowest energy is chosen as the optimal critical scale, and the correction factor is adaptively adjusted according to the critical scale. Following that, an improved threshold function with a variable factor is proposed, with the variable factor being controlled by the critical scale in order to adapt to different disturbance types’ denoising. The simulation results show that the proposed method outperforms existing methods for denoising various types of power quality disturbance signals, significantly improving SNR and minimizing MSE, while retaining critical information during disturbance mutation. Meanwhile, the effective location of the denoised signal based on the proposed method is realized by singular value decomposition. The minimum location error is 0%, and the maximum is three disturbance points.

Keywords: wavelet denoising; power quality disturbance; energy optimization; adaptive threshold; improved threshold function (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: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/9/3081/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/9/3081/ (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:3081-:d:800151

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 ().