A recognition method for abnormal substation noise based on non-intrusive decomposition technology

This study proposes a recognition method for abnormal substation noise based on non-intrusive decomposition technology. The aim is to achieve efficient separation and accurate identification of substation noise through advanced signal processing and deep learning technologies. Firstly, this method uses non-contact sound sensors to collect noise data at the plant boundary, including the operating noise of transformers and reactors. Subsequently, after data preprocessing and multi-scale decomposition of wavelet transform, combined with short-time Fourier transform (STFT) and Mel-frequency cepstral coefficients (MFCC) to extract time-frequency features, and then conduct joint analysis of time series and spectrum features through convolutional neural network (CNN). Finally, the model judges the noise state and distinguishes normal, early fault and fault states. Compared with traditional methods, this study has significant advantages in reducing monitoring costs, adapting to complex environments and accurately locating abnormal noise, providing an effective solution for fault detection of substation equipment.

I. INTRODUCTION

Substations play a crucial role in the power system, but their operation and construction processes can bring about noise pollution problems [1]. The vibration and noise generated when equipment such as transformers and reactors are in operation not only may affect the normal operation of the equipment but also can interfere with the lives of residents around the substation and even endanger health in severe cases. In addition, these noises contain a large amount of information about the operating status of equipment, which can reflect both the normal working state of the equipment and may also indicate potential faults [2]–[3]. Therefore, accurately identifying and decomposing the operating noise of substation equipment is of great significance for ensuring the stable operation of the power system, improving the level of equipment operation and maintenance, and reducing the risk of failures.