Research on Fault Location Technology of Transformer Acoustic Feature Recognition and Field Perception Data Fusion under Small Sample Parameters
Abstract
Reliable transformer fault diagnosis under limited fault samples remains a significant challenge in intelligent power systems. To address the difficulties associated with weak fault signatures, severe environmental interference, and insufficient training samples, this study investigates transformer fault location technology based on acoustic feature recognition and field perception data fusion. The generation mechanism and propagation characteristics of transformer acoustic signals are first analyzed, and an improved time–frequency feature extraction method is developed to enhance feature representation under small-sample conditions. A multi-physics data fusion framework integrating acoustic, vibration, and electrical sensing information is then established, and a dedicated attention mechanism is designed to achieve deep feature fusion across heterogeneous data sources. Finally, an enhanced deep neural network model is employed for accurate fault localization and condition identification. Experimental results demonstrate that the proposed framework effectively improves fault recognition performance and location accuracy under small-sample constraints. The study provides technical support for intelligent power equipment monitoring and offers methodological references for signal propagation analysis, sensor fusion, and electromagnetic condition monitoring systems.