Active Noise Control of Power Transformer Based on Time Series Forecasting

As urbanization accelerates, substations have become a part of residential and downtown areas. The low-frequency noise generated by transformers has increasingly become a major source of complaints from neighboring residents. Due to the long wavelength of low-frequency noise, traditional passive noise reduction technology is bulky, expensive and limited in effectiveness. Active noise control (ANC) has gained attention due to its compact size, light weight, low cost, and excellent ability to reduce low-frequency noise. However, the precision of ANC relies on the control algorithm, and conventional least mean square-based algorithms still face challenges such as sluggish convergence, susceptibility to dispersion, and poor stability. In light of the time-varying characteristics of transformer noise, this paper proposes a machine learning-based algorithm for generating inverse noise waveform through time-series forecasting, and this approach aims to achieve rapid and stable noise reduction. The experimental result shows that the algorithm achieves noise reduction effects of 49.67 dB, and the time used for training and prediction is 1.78 s and 1.64 s, which shows a promising solution for transformer noise reduction.