Chen Fei, Tian Wanfu, Zhang Liyao, Li Jiazheng, Ding Chen, Chen Diyi, Wang Weiyu, Wu Fengjiao, Wang Bin
Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China.
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China.
Entropy (Basel). 2022 Aug 16;24(8):1135. doi: 10.3390/e24081135.
In order to accurately diagnose the fault type of power transformer, this paper proposes a transformer fault diagnosis method based on the combination of time-shift multiscale bubble entropy (TSMBE) and stochastic configuration network (SCN). Firstly, bubble entropy is introduced to overcome the shortcomings of traditional entropy models that rely too heavily on hyperparameters. Secondly, on the basis of bubble entropy, a tool for measuring signal complexity, TSMBE, is proposed. Then, the TSMBE of the transformer vibration signal is extracted as a fault feature. Finally, the fault feature is inputted into the stochastic configuration network model to achieve an accurate identification of different transformer state signals. The proposed method was applied to real power transformer fault cases, and the research results showed that TSMBE-SCN achieved 99.01%, 99.1%, 99.11%, 99.11%, 99.14% and 99.02% of the diagnostic rates under different folding numbers, respectively, compared with conventional diagnostic models MBE-SCN, TSMSE-SCN, MSE-SCN, TSMDE-SCN and MDE-SCN. This comparison shows that TSMBE-SCN has a strong competitive advantage, which verifies that the proposed method has a good diagnostic effect. This study provides a new method for power transformer fault diagnosis, which has good reference value.
为了准确诊断电力变压器的故障类型，本文提出了一种基于时移多尺度气泡熵（TSMBE）与随机配置网络（SCN）相结合的变压器故障诊断方法。首先，引入气泡熵以克服传统熵模型对超参数依赖过重的缺点。其次，在气泡熵的基础上，提出了一种测量信号复杂度的工具——时移多尺度气泡熵。然后，提取变压器振动信号的时移多尺度气泡熵作为故障特征。最后，将故障特征输入到随机配置网络模型中，以实现对不同变压器状态信号的准确识别。将所提方法应用于实际电力变压器故障案例，研究结果表明，与传统诊断模型MBE - SCN、TSMSE - SCN、MSE - SCN、TSMDE - SCN和MDE - SCN相比，TSMBE - SCN在不同折叠数下的诊断率分别达到了99.01%、99.1%、99.11%、99.11%、99.14%和99.02%。这种比较表明TSMBE - SCN具有很强的竞争优势，验证了所提方法具有良好的诊断效果。本研究为电力变压器故障诊断提供了一种新方法，具有良好的参考价值。
