An Optimized Fault Feature Extraction Method for PV Grid-Connected T-Type Three Level Inverter

Meng, Xuesong; Ma, Mingyao; Ling, Feng; Wang, Fusheng; Zhang, Zhenbin; Zhang, Pinjia

doi:10.1109/iciea48937.2020.9248346

Cited by 2 publications

(1 citation statement)

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“…Under normal circumstances, two problems need to be considered when decomposing the characteristics of wavelet packet extraction mode: one is the selection of the wavelet basis function, and the other is the determination of the number of decomposition layers. Due to the complex composition of the transformer vibration signal, the DB5 wavelet basis function [6] is used for the wavelet packet decomposition of the vibration compression signal.…”

Section: Distribution Transformer Compression Signal Feature Extractionmentioning

confidence: 99%

Transformer Vibration Signal Feature Extraction Based on Compressed Sensing and Wavelet Packet

Ning

Chi

Xiao

et al. 2023

J. Phys.: Conf. Ser.

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Transformers are important equipment in the power system. Aiming at the collection, transmission, storage, and processing of massive high-dimensional vibration data in the process of transformer vibration online monitoring, this paper proposes a transformer vibration signal feature extraction method based on compressed sensing and wavelet packets. Firstly, the PartHadamard measurement matrix is used to compress the transformer vibration signal, and then the characteristics of the transformer vibration signal are extracted based on the wavelet packet decomposition and normalized wavelet information entropy. The proposed method completes feature extraction on the measured vibration data of the transformer, and three classification algorithms are applied to carry out simulation experiments. The results show that the proposed feature extraction method can effectively extract the vibration characteristics reflecting the operating state of the transformer while greatly reducing the dimension of vibration data, which provides a reference for the online monitoring of transformer vibration.

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Section: Distribution Transformer Compression Signal Feature Extractionmentioning

confidence: 99%