Deformation Analysis Method for Substation Expansion Joints Based on Multi-Scale Feature Fusion

As a crucial component in the power transmission system, the health status of substation expansion joints directly impacts the safe and stable operation of the entire power grid. This paper introduces a method based on multi-scale feature fusion for efficient and accurate analysis of the deformation of substation expansion joints. Initially, this method employs high-resolution image acquisition technology to capture surface morphological information of the expansion joints, and utilizes a multi-scale convolutional neural network to extract features from different scales. Subsequently, the multi-scale features are integrated through a feature fusion module, enhancing the model's ability to recognize subtle deformation patterns. Additionally, this paper incorporates time series analysis technology, modeling the dynamic behavior of the expansion joints in conjunction with historical data, thereby further improving prediction accuracy. Experimental results demonstrate that the proposed multi-scale feature fusion method can effectively detect various deformation conditions of the expansion joints, including but not limited to excessive extension, compression, fatigue cracks, and corrosion, providing robust technical support for preventive maintenance of the power system. This research holds significant theoretical and practical value in enhancing the intelligent monitoring level of power equipment.