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In a synchronous generator excitation control system, the damping capacity of the system is generally weakened during high-accuracy regulation of terminal voltage, which is against system stability. In this study, an excitation control method based on iterative learning control (ILC) with initial state learning and model-free adaptive grey prediction control (MFAGPC) was proposed to achieve high-accuracy voltage regulation and system stability. Based on the third-order dynamic model of generators, an excitation control system was constructed. The system used ILC as primary controller and MFAGPC as secondary controller to design the terminal voltage and rotor speed, respectively. Furthermore, the influences of ILC and MFAGPC on the regulation accuracy of terminal voltage and system stability were discussed. The proposed control method was validated by simulation and experimentation. The results demonstrate that under the conventional PID+PSS, the overshoot and settling time of the terminal voltage are 32% and 2.6s, and the rotor speed undergoes 6 oscillations in 4.3s before the system returns to a stable state. Due to the synergic effect of ILC and MFAGPC, the overshoot and settling time of the terminal voltage are 7% and 0.7s, and the rotor speed just undergoes 3 oscillations in 2.5s before the system returns to steady state. The proposed control method assures that the system achieves adequate regulation accuracy of terminal voltage in a short time, overcomes influences of internal and external disturbances on the system, and enhances system stability. This study provides a reference for further studies on multigoal control problems of power systems.

Generator Excitation Control Method based on Iterative Learning Control with Initial State Learning and Model - free Adaptive Grey Prediction Control