High-order sliding mode control of a doubly salient permanent magnet machine driving marine current turbine

Due to the harsh and changeable marine environment,one low speed stator-permanent magnet machine named doubly salient permanent magnet machine with toothed pole is applied for marine current energy conversion system.Indeed,this machine has simple structure,intriguing fault tolerance,and higher power density,which could adequately satisfy the different complicated operation conditions.However,its permanent magnet flux-linkage has the same variation period as the inductance which leads to a strong nonlinear coupling system.Moreover,the torque ripple caused by this special characteristics,uncertainty of system parameters and disturbance of load greatly increases the difficulty of control in this strongly coupling system.Consequently,the classical linear PI controller is difficult to meet the system requirement.In this paper,the high-order sliding mode control strategy based on the super-twisting algorithm for this system is creatively utilized for the first time.The stability of the system within a limited time is also proved with a quadratic Lyapunov function.The relative simulation results demonstrate convincingly that,the high-order sliding mode control has little chattering,high control accuracy and strong robustness.

基于改进微分进化算法的风光互补系统发电容量优化配置

为有效兼顾风光互补发电系统的可靠性与经济性,以年缺电负荷率最小及微网系统投资建设成本最低为目标函数,以储能电池充放电次数、微网功率平衡以及电池充放电上下限等为约束条件,搭建风光互补系统发电容量优化模型。针对缺电负荷率、系统成本2个目标函数相悖的特点,考虑将帕累托非劣排序操作引入至常规微分进化算法,并对常规算法中的变异策略进行了相应改进,使得改进后的算法在具备多目标寻优能力的同时,寻优速度也得到了较为显著的提升。最后,应用所提改进多目标微分进化算法对风光互补系统发电容量优化模型进行求解,算例结果验证了该文模型的合理性和所提算法的有效性。