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Adaptive Quantization Control for Multi-machine Power System with SVC and Unknown Disturbance

In this paper, a control method combined with dynamic surface and sliding mode based on input quantization is proposed for multi-machine power system with SVC. The quantization of state variables solves the signal transmission problem of multi-machine power system in network control environment. In this paper, the error terms and unknown disturbances of multi-machine power system modeling are estimated by neural network. The boundedness of quantization errors and estimated parameters in closed-loop system is analyzed by some lemmas. Finally, the effectiveness of the tracking scheme is verified by simulation results.