This paper introduces IVLIFN-APS, a novel image fusion network for power system monitoring that incorporates attention mechanisms, gradient residuals, and dense blocks. The architecture features separate branches for visible and infrared processing, each containing Gradient Residual Dense Blocks and Attention Learning Modules before feature concatenation. Experiments on power scene datasets demonstrate superior performance in visual quality, target detection accuracy, and semantic consistency compared to state-of-the-art methods.