Shang Haikun, Huang Tao, Wang Zhiming, Li Jiawen, Zhang Shen
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China.
Entropy (Basel). 2024 Aug 23;26(9):721. doi: 10.3390/e26090721.
A mechanical vibration fault diagnosis is a key means of ensuring the safe and stable operation of transformers. To achieve an accurate diagnosis of transformer vibration faults, this paper proposes a novel fault diagnosis method based on time-shift multiscale increment entropy (TSMIE) combined with CatBoost. Firstly, inspired by the concept of a time shift, TSMIE was proposed. TSMIE effectively solves the problem of the information loss caused by the coarse-graining process of traditional multiscale entropy. Secondly, the TSMIE of transformer vibration signals under different operating conditions was extracted as fault features. Finally, the features were sent into the CatBoost model for pattern recognition. Compared with different models, the simulation and experimental results showed that the proposed model had a higher diagnostic accuracy and stability, and this provides a new tool for transformer vibration fault diagnoses.
机械振动故障诊断是确保变压器安全稳定运行的关键手段。为实现对变压器振动故障的准确诊断，本文提出了一种基于时移多尺度增量熵（TSMIE）与CatBoost相结合的新型故障诊断方法。首先，受时移概念的启发，提出了TSMIE。TSMIE有效解决了传统多尺度熵粗粒化过程中信息丢失的问题。其次，提取不同运行工况下变压器振动信号的TSMIE作为故障特征。最后，将这些特征输入到CatBoost模型中进行模式识别。与不同模型相比，仿真和实验结果表明，所提模型具有更高的诊断精度和稳定性，为变压器振动故障诊断提供了一种新工具。
