Fault Diagnosis of Grid-Connected Microgrid Lines Based on AVMD and CNN

Linyao FU; Chunhua LI; Benke WANG; Yongshuang BAN

doi:10.16513/j.2096-2185.DE.2308403

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Distributed Energy ›› 2023, Vol. 8 ›› Issue (4) : 20-28. DOI: 10.16513/j.2096-2185.DE.2308403

Basic Research

Fault Diagnosis of Grid-Connected Microgrid Lines Based on AVMD and CNN

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Abstract

In order to improve the fault diagnosis accuracy of three-phase microgrid lines, a fault diagnosis and classification method based on adaptive variational mode decomposition (AVMD) and convolutional neural network (CNN) is proposed in this paper. Firstly, a microgrid radial structure model including wind, light and water system is established. AVMD is used to decompose the original fault signal into multiple modal components, where the parameters of variational mode decomposition (VMD) are optimized by aquila optimizer (AO). Only a few of the modes retain the fault signal information. Effective weighted peak relevance index (EWPR) is used to select the modal components, and the three modes that can best retain the fault information are selected as sensitive modes. The influence of noise and other irrelevant modes is eliminated, and CNN is used to diagnose and classify the circuit faults of the microgrid. 110 groups of fault data are generated for training and verification of neural network. The results show that 21 groups of data in 22 groups of verification data sets are classified correctly, and the fault diagnosis accuracy of this method reaches 95.46%.

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adaptive variational mode decomposition (AVMD) / effective weighted peak relevance (EWPR) index / aquila optimizer (AO) / convolutional neural networks (CNN) / fault diagnosis classification

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Linyao FU , Chunhua LI , Benke WANG , et al. Fault Diagnosis of Grid-Connected Microgrid Lines Based on AVMD and CNN[J]. Distributed Energy Resources. 2023, 8(4): 20-28 https://doi.org/10.16513/j.2096-2185.DE.2308403

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