Abstract: With the rapid development of smart grids, large-scale investment in measurement equipment for monitoring the operation of power grids, the monitoring data such as massive operation images generated by them is difficult to be effectively utilized due to problems such as large scale, high dimension and data redundancy. In order to further improve the analysis and application ability of power big data, this paper proposes a power grid operation image data compression method based on deep learning. Considering the coupling correlation of power grid image monitoring data in time series, the power grid operation image data is compressed through convolutional neural network, effectively reducing the redundancy of power grid operation image data. Compared with other methods, the image data compression model based on convolutional neural network does not rely on manual data feature extraction and engineering experience, and can directly use the grayscale function of the original image data collected from the power grid as the input of the model, and the data The feature extraction and classification are combined into one, to achieve efficient and convenient compression of power grid operation image data. The effectiveness of the method proposed in this paper is verified by simulation. The results show that the proposed method has strong advantages in power grid image compression efficiency and compression accuracy compared with other neural networks.