To address the challenges of rapid voltage fluctuations and operational economy in new power systems with high wind power integration under sandstorm weather, this paper proposes a multi-timescale cooperative voltage control strategy for new power systems. On the second-level timescale, a discrete state-space model of wind turbines and reactive power devices is established considering sudden wind speed changes. Model predictive control (MPC) is then used to rapidly calculate the optimal reactive power references for wind turbines, static var generator (SVG), and on-load tap changer (OLTC), thereby effectively ensuring rapid stabilization of the grid-connection point voltage. On the minute-level timescale, a two-stage topology reconfiguration method is adopted. A feasible radial network is first generated through a sequential switch opening strategy, followed by iterative optimization via a switch exchange strategy. This approach rapidly identifies the optimal switch configuration to minimize network losses and improve operational economy. Simulation results demonstrate the effectiveness of the proposed strategy in voltage regulation and loss reduction, highlighting its capability to enhance the robustness of new power systems under sandstorm weather.

In response to the severe challenges posed by extreme sandstorm weather to the operational safety of WTs and grid stability, this paper proposes an MPC-based power optimization control strategy for WFs. Simulation results indicate that, compared with the traditional PD strategy, the proposed MPC strategy significantly reduces the active power fluctuations of individual WTs, smoothly tracks grid dispatch orders with an overall power tracking accuracy improvement, and effectively lowers the operational risk index of turbines across the farm (ranging from 6.90% to 57.14% for the ten evaluated turbines). Furthermore, the proposed strategy substantially mitigates the angular acceleration fluctuation amplitude of the drive train components (e.g., reducing peak angular accelerations of drive-train masses by up to 35%) and reduces the fore-aft and lateral displacement oscillations of the tower top (reducing peak displacement variations by approximately 25% and 40%, respectively), providing comprehensive structural load mitigation while ensuring WF power output stability and grid safety. This study provides a theoretical basis and technical approach for the intelligent operation and risk prevention and control of WFs under extreme meteorological conditions.