False data injection attack (FDIA) in power information system affects the normal operation of power grid by maliciously tampering with the state data of corresponding physical system. This paper proposes a false data injection attack detection method based on dynamic kernel principal component analysis (DKPCA), in order to solve the time correlation of FDIA events in power information system (dynamic) problem, and the problem that it is difficult to separate nonlinear variables. This method solves the dynamic autocorrelation between variables by constructing a dynamic augmented matrix, uses the kernel matrix to map nonlinear variables into high-dimensional space and convert them into linear variables, introduces principal component analysis to establish DKPCA model, obtains the control limit of statistics, and judges whether there is a fault by detecting data in real time. The experimental simulation is carried out on TEEE-30 node system. Compared with KPCA, PCA, NPE, TNPE and other detection methods, the results show that the detection rate of DKPCA model is as high as 100%, while maintaining a low false positive rate of 0.2%. It is proved that the proposed method can detect the attack data in power information system in real time, effectively avoid fault omission and ensure the data security of power information system.