Optimization Research on Abnormal Diagnosis of Transformer Voiceprint Recognition based on Improved Wasserstein GAN

Abstract

Transformer is an important infrastructure equipment of power system, and fault monitoring is of great significance to its operation and maintenance, which has received wide attention and much research. However, the existing methods at home and abroad are based on model analysis for detection and diagnosis techniques, and their application is limited by the maturity and applicability of expert knowledge. In recent years, deep learning artificial intelligence has made important breakthroughs, however, the relevant research is still mainly focused on the field of speech, and intelligent diagnostic techniques on machine voice patterns have just begun. Therefore, this article adopts an unsupervised learning anomaly detection method based on WGAN-GP to diagnose the electrical and mechanical anomalies of transformer equipment. The experimental results show that the method can effectively identify transformer anomalies and provide an idea for artificial intelligence-based machine voice pattern recognition.