电力系统保护与控制2026，Vol.54Issue(2)：48-57,10.DOI:10.19783/j.cnki.pspc.250491
基于SPSM-MPC的海上风电系统陆上换流站优化控制策略
Optimized control strategy for onshore converter stations of offshore wind power systems based on SPSM-MPC
摘要
Abstract
In offshore wind power grid-connected systems,onshore converter stations based on modular multilevel converters are prone to disturbances under operating conditions such as sudden variations in wind power output,equipment switching,and grid voltage sags.To address this issue,an optimized control strategy integrating slope passivity-based sliding mode control and model predictive control(SPSM-MPC)is proposed.The strategy is centered on inner current loop control.A passivity-based sliding mode(PSM)controller is first employed to establish the basic control framework,upon which a slope regulation mechanism is introduced to construct the slope passivity-based sliding mode(SPSM)control strategy.Meanwhile,model predictive control(MPC)is organically embedded into the modulation algorithm framework.Simulation models are used to compare the dynamic performance of three control strategies:conventional PI,PSM,and SPSM-MPC,under normal system operation and three typical disturbance conditions.The results show that the SPSM-MPC strategy reduces the steady-state output current THD to 4.45%,shortens the response time to sudden wind power variations to 2 ms,and reduces the active power stabilization time to 0.15 s under grid voltage sag conditions.Through the synergistic effect of the slope mechanism and predictive control,the SPSM-MPC strategy effectively enhances system robustness under dynamic disturbances,providing a new control scheme for the stable operation of offshore wind power grid-connected systems.关键词
风电并网/模块化多电平换流器/无源滑模控制/模型预测控制/最近电平逼近调制Key words
wind power grid connection/modular multilevel converter/passivity-based slide mode control/model predictive control/nearest level modulation引用本文复制引用
李慧,钱磊,范新桥,魏玲..基于SPSM-MPC的海上风电系统陆上换流站优化控制策略[J].电力系统保护与控制,2026,54(2):48-57,10.基金项目
This work is supported by the Natural Science Foundation of Beijing(No.3232045). 北京市自然科学基金项目资助(3232045) （No.3232045）
