Research on Transformer Fault Diagnosis Based on Voiceprint Signal

Abstract

Abstract As an important part of the power system, the operating status of the transformer will have a direct impact on the stability and reliability of the power system. In view of the problems of high diagnosis cost and low accuracy of diagnosis results in existing fault diagnosis technology, this paper takes advantage of the obvious difference between the voiceprint signal of the transformer under normal and fault operating conditions and applies it to transformer fault diagnosis, which can effectively reflect its internal working status and fault conditions, helping operation and maintenance personnel promptly discover equipment defects and locate fault causes. In order to accurately realize transformer fault diagnosis, this paper uses the improved hybrid frog leaping algorithm to optimize the fault diagnosis algorithm of support vector machine parameters for fault diagnosis, which further improves the accuracy of fault diagnosis and is of great significance for accurately identifying transformer fault states.