With the continuous expansion and increasing complexity of modern power grids, there is a need for a technology that can integrate and process multi-source information to meet the demands of large-scale and highly complex information processing. This is essential to enhance the efficiency and security of power grid operations. To this end, a multi-source information fusion method for power grid operation based on fuzzy sets is designed. The collection of multi-source information for power grid operation is implemented through various sensors, including electric sensors, pressure sensors, and humidity sensors. The data collection methods of these sensors inevitably introduce noise into the collected data. To address this, wavelet denoising methods are employed to reduce noise and extract effective information from the power grid operation data. A multi-source information fusion method combining the fuzzy similarity matrix in fuzzy set theory and the Dempster-Shafer(D-S) evidence theory is designed to achieve the fusion of multi-source information in power grid operation. Experimental test results indicate that as the number of data types increases, the maximum confidence level of this method is in a growth phase. The maximum confidence level of multi-source information fusion reaches 0.94, demonstrating that the fusion results are reliable and applicable to the fusion of various types of data. After adding noise levels of 5 dB, 10 dB, 15 dB, 20 dB, and 25 dB, the maximum confidence level of multi-source information fusion using the designed method only experiences a minimal decrease. This indicates that the method has good robustness in multi-source information fusion. Additionally, the high information entropy values suggest that the fused information is richer in content.