Abstract

The real-time monitoring and control strategy of transformer noise based on adaptive algorithm is investigated, and a method combining physical noise reduction and active noise control is proposed. The study reports the main sources of transformer noise and its propagation pathways, and describes the application of adaptive algorithms in noise monitoring models, including specific steps such as signal pre-processing, feature extraction, model training and noise control. The experiment is carried out in a 220kV substation, and the results show that the strategy is better than the existing methods in terms of noise reduction effect and system stability. Through the adaptive filtering algorithm, the control parameters can be adjusted in real time to achieve efficient control of transformer noise. The conclusion states that the strategy has significant advantages in terms of effective noise reduction, high stability, real-time monitoring and cost-effectiveness, and provides a new solution for noise control of smart grid equipment, which has important engineering application value.