Abstract: A novel online identification-detection method based on watermarking and particle swarm optimization(PSO) algorithm is proposed to address the issue of false data injection (FDI) attack detection in the load frequency control (LFC) system with parameter uncertainty. Firstly, a state estimation model for the LFC system is established based on Kalman filtering. To assist the \chi ^2 detector in identifying FDI attacks, a transmission strategy of adding a pseudo-random number watermarking matrix during information transmission is proposed. Prior to transmission, the transmitting terminal multiplies the measurement to be transmitted by a pseudo-random number matrix. Subsequently, the receiving terminal decrypts the received data to retrieve the genuine information. Compared with traditional methods, this strategy does not increase computational complexity or sacrifice estimation performance in the absence of attacks. In the presence of FDI attacks, the system measurement is maliciously tampered with. In the decrypted measurement, there exists a coupling relationship between the watermarking matrix and the injected attack signal, which can be easily recognized by the \chi ^2 detector and thus ensures the efficient and stable operation of the system. In addition, considering the parameter uncertainty problem of the LFC system, the PSO algorithm is adopted for parameter identification to improve the matching degree between the estimation model and the actual system. Finally, an online identification-detection method combining watermarking and PSO algorithm is proposed. The effectiveness of the analytical results is verified through simulations on a two-area LFC system.