针对传统OLTC故障诊断特征选择较为单一、故障识别准确率较低的缺陷，从

Aiming at the defect that the traditional OLTC fault diagnosis feature selection is relatively simple and the fault recognition accuracy is low, from the perspective of multi-feature fusion, a high-dimensional feature set of time domain, energy and multi-scale weighted arrangement entropy is constructed

In view of the defects of selecting the relatively single and most accurate fault recognition features of traditional OLTC, the high-speed collection of time domain, energy and multi-scale weighted arrangement entropy is constructed from the perspective of multi-feature fusion.

Aiming at the defect of single feature selection and low accuracy of fault recognition in traditional OLTC fault diagnosis, a high Vitt collection of time domain, energy and multi-scale weighted permutation entropy is constructed from the perspective of multi feature fusion<br>