Fault Diagnosis of Ship Regional Distribution Power System based on Multi Granularity ELM

Shuangshuang Zhou ； Yan Zhang

《International Core Journal of Engineering》 8卷6期 (2022/06) Pp. 828-836

https://doi.org/10.6919/ICJE.202206_8(6).0110

摘要

In view of the characteristics of multi-type and complex samples in fault diagnosis of marine regional distribution power system, a multi granularity extreme learning machine (MGELM) model is proposed to apply to fault diagnosis. The simulation model of ship ring area distribution system is built by SIMULINK, the voltage and current of distribution line and the energy signal characteristics of generator set are extracted by wavelet packet decomposition; Based on the multi granularity feature subset obtained by neighborhood rough set attribute reduction, the ELM network is improved, and combined with the reliability weighted voter, the MGELM model is constructed to diagnose the ship regional distribution fault. Compared with extreme learning machine, support vector machine and random forest model, the experimental results show that the proposed method improves the model training stability by more than 80% compared with extreme learning machine, it has higher accuracy and reliability compared with all comparison models in classification performance, and can still maintain the optimal classification effect when the samples are unbalanced.

關鍵字

Ship Regional Distribution Power System ； Fault Diagnosis ； Attribute Reduction ； Extreme Learning Machine ； Wavelet Packet Decomposition

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