Abstract: With the widely deployment of information techniques in smart grid, it is quite important to automatically detect the bad data, e.g., malicious injection data and unfunctional sensor data, from daily observations. In this paper, we propose a novel approach for bad data detection in smart grid based on multi-view low-rank analysis. Specifically, the proposed method estimates the grid state by analyzing the data collected from multiple sources. A low-rank function is learned to unveil the shared true data from observations, and the sparsity of data is applied to formulate bad data. Furthermore, an iterative optimization algorithm is proposed to solve the objective function. At last, extensive experiments on several IEEE bus systems verify the superiority of the proposed method.