Acoustic signal enhancement algorithm for transformer based on singular value decomposition and spectral clustering

Abstract

Transformers are critical components in power transmission and transformation systems, and their stable operation directly impacts the safety and reliability of the power grid. Due to its non-contact nature and ease of deployment, acoustic signal detection method has been widely adopted for power transformer condition monitoring. However, acoustic signals acquired from outdoor transformers are highly susceptible to environmental interference. In particular, the spectral overlap between cooling fan noise and transformer-generated acoustic signals poses a significant challenge for anomalous sound detection. To address this issue, we propose an acoustic signal enhancement method for transformers based on singular value decomposition (SVD) and spectral clustering. First, by leveraging the relative stability of the amplitude and phase across transformer acoustic signal frequency components over short periods, we apply threshold-based filtering to identify noisy frequency points, combining this with spectral subtraction for preliminary signal enhancement. Next, we design an SVD method incorporating elbow detection to attenuate noise while preserving weak yet informative signal components. Finally, to address the critical challenge of separating the intertwined singular values corresponding to fan noise and transformer acoustic signals, we propose a spectral clustering algorithm based on mutual information. By exploiting the amplitude filtering characteristics of SVD, this algorithm achieves an effective separation of fan noise and transformer acoustic signals. Experimental validation was performed on a field-collected dataset of transformer acoustic signals. The results demonstrate that the proposed method exhibits superior overall performance in both noise suppression and effective feature extraction.