For the problem of the existence of time delay in information transmission in power system which affects the stability of power system, this paper takes the remote control of the stability of power system as the research purpose. Radial Basis Function (RBF) neural network is introduced to define the adaptive control law for discrete nonlinear systems. Thus, a discrete adaptive neural network is constructed to estimate the unknown parameters and uncertainties within the power system. Then for the single machine infinity power system, establish a more realistic nonlinear generalized system mathematical model, for the analysis of this paper to provide a theoretical basis for research. For the optimization of power system stabilizer when the power system generates low-frequency oscillations, the Mothfly Flame (MFO) algorithm is selected for the coordinated optimization and tuning of controller parameters. Based on the multi-objective function, the optimal parameters of the controller are optimally solved under different time delays. Combining the above, the design of remote control strategy for power system based on adaptive control is completed. In the numerical simulation experiment, the coordinated power system controller starts to converge in about 1s, and the overall oscillation amplitude is small. The excellent robustness of the power system controller in remote control based on adaptive control algorithm is demonstrated.
