Citation

Goh, Hui Hwang and Liao, Ling and Zhang, Dongdong and Dai, Wei and Lim, Chee Shen and Kurniawan, Tonni Agustiono and Goh, Kai Chen and Cham, Chin Leei (2022) Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy Optimization. Energies, 15 (9). p. 3081. ISSN 1996-1073

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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.

Item Type:	Article
Uncontrolled Keywords:	Wavelet denoising, power quality disturbance, energy optimization, adaptive threshold, improved threshold function
Subjects:	Q Science > QC Physics > QC770-798 Nuclear and particle physics. Atomic energy. Radioactivity
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	05 Jul 2022 06:54
Last Modified:	05 Jul 2022 06:54
URII:	http://shdl.mmu.edu.my/id/eprint/10097

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