Intelligent Predictive Maintenance of Dry-Type Transformer Based on Vibration

Abstract

The normal operation of the transformer is an important guarantee for the safe operation of the whole power network. Usually, the periodic maintenance work increases the workload of the operation and maintenance personnel. In this paper, an intelligent predictive and maintenance system for dry-type transformer based on vibration is proposed to monitor the vibration state of the transformer. The fractional-order Kalman filter and normalization method are used to preprocess the vibration data to reduce noise interference, and then the fault is classified by one-dimensional convolution neural network. On this basis, an improved grey Verhulst model is established to predict the fault time of the transformer. The experimental results show that the classification accuracy of the one-dimensional convolution neural network can reach more than 95%, and the improved grey Verhulst model can predict the fault of the dry-type transformer one week in advance.