Intelligent Fault Identification Method for Distribution Network Power Equipment Based on 5G Technology and Association Rules

The current conventional distribution network power equipment fault identification method mainly locates the abnormal part in the infrared image to mark the fault node, which leads to poor identification accuracy due to the low degree of image enhancement and denoising processing. In this regard, an intelligent fault identification method for distribution power equipment based on 5G technology and association rules is proposed. By calculating the confidence degree of the sample data, the classification mining process is performed on the power equipment data. The image data is enhanced and denoised by combining grayscale information method and Gaussian filtering algorithm, and the covariance matrix is constructed to analyze the extracted fault feature parameters. In the experiments, the proposed recognition method is verified. The experimental results show that the proposed method has a high recognition accuracy and ideal recognition effect for fault identification of distribution network power equipment.