Transformer State Monitoring System Based on TCSM

With the increasing demand for transformer health status monitoring in power systems, traditional monitoring methods have significant limitations in terms of response speed and accuracy. This paper proposes a transformer state monitoring system based on TCSM, aiming to achieve real-time monitoring and diagnosis of transformer operating conditions through non-intrusive sound analysis. The system comprises three layers: data collection, edge computing, and software application. The data collection layer uses high-sensitivity acoustic sensors and vibration sensors to capture the sound wave signals of transformers and preprocess them to improve data quality. The edge computing layer extracts features from the sound signals using advanced feature extraction algorithms such as Mel-frequency cepstral coefficients (MFCC) and analyzes the signals using the TCSM model to identify abnormal patterns and make local decisions. The software application layer provides an intuitive user interface and remote monitoring capabilities, combining historical data analysis and trend prediction to support maintenance decisions. Experimental results show that the proposed system significantly outperforms traditional methods in terms of real-time performance, accuracy, and intelligence, effectively enhancing transformer operation and maintenance efficiency and the reliability of the power grid.