Research and Application of AI-Based Voiceprint Recognition and Defect Active Early Warning Technology for Large Power Transformers

Abstract

Large power transformers and reactors are core equipment in power transmission and transformation systems. Early identification and active early warning of latent faults are critical to ensure the safe and stable operation of power grids. Aiming at technical shortcomings such as failure of relay protection to provide early warning, difficulty in online monitoring of insulation parameters, limited information of temperature monitoring, and contact requirement for vibration detection, this paper proposes the integration of voiceprint recognition and artificial intelligence in equipment condition monitoring. By collecting voiceprint signals of on-site operating equipment, a standardized big data sample library is constructed, and a deep learning-based voice recognition cloud platform is developed to optimize feature extraction and pattern matching algorithms. A high-reliability microphone array sensing and acquisition system is built under strong electromagnetic interference, with research on primary sound source enhancement and fault source localization to realize active identification and transmission of abnormal data. The voiceprint characteristics under typical conditions including normal operation, mechanical defects, partial discharge, and on-load tap changer faults are revealed. A set of non-contact online monitoring and intelligent decision-making system is established with defect identification criteria and graded early warning mechanism. The research results achieve early detection, accurate identification and active early warning of transformer (reactor) defects, reduce fault risks and unplanned outages, improve the level of intelligent operation and maintenance, and possess significant engineering application value and promotion prospects.