Research on Real-time Monitoring and Early Warning of Abnormal Operation Status of Main Transformer Equipment Under Multi sensor Information Fusion

Traditional monitoring and warning methods for abnormal operation of main transformer equipment have problems such as poor adaptability to complex working conditions, susceptibility to noise interference, and insufficient accuracy and timeliness of warning. To this end, research is being conducted on real-time monitoring and early warning methods for abnormal operation of main transformer equipment under multi-sensor information fusion. Firstly, to address the issue of communication signals being easily interfered with during the operation of the main transformer equipment, filtering algorithms are used for denoising to improve signal quality; in the multi-sensor information fusion process, sound and vibration sensor data are integrated, and fusion algorithms are used to explore potential correlations between the data, achieving comprehensive monitoring of the operating status of the main transformer equipment; based on the fused data, a warning model is constructed, which combines historical data and real-time monitoring information to determine whether the equipment is abnormal in real time and issue warnings. The experimental results show that while maintaining a monitoring accuracy of 97%, the warning response time of this method is controlled at a low level, with a maximum of no more than 5 ms, which can provide more timely and effective guarantees for the safe and stable operation of the main transformer equipment.