针对五电平中点钳位(Neutral Point Clamped,NPC)变流器直流电容电压平衡问题,分析了五电平NPC变流器空间矢量调制中各开关矢量对直流电容电压平衡的影响,阐述了分调制比区域实现五电平NPC变流器空间矢量调制的控制思想,提出了一种新型...针对五电平中点钳位(Neutral Point Clamped,NPC)变流器直流电容电压平衡问题,分析了五电平NPC变流器空间矢量调制中各开关矢量对直流电容电压平衡的影响,阐述了分调制比区域实现五电平NPC变流器空间矢量调制的控制思想,提出了一种新型直流电容电压自平衡的五电平NPC变流器空间矢量调制策略。该策略将空间矢量图按调制比分区,在低调制比区域(mSV≤0.5),采用目标函数与参考电压分解相结合的方法,在高调制比区域(mSV>0.5),采用平衡矢量优化选择法(Optimized Balancing Vectors Selection,OBVS),分别实现了直流电容电压自平衡的五电平空间矢量调制。仿真和实验表明本文提出的新型调制策略兼具直流电容电压平衡控制功能,计算量小,实现简单,控制效果好。展开更多
A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm ad...A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.展开更多
基金Project(2010ZC13012) supported by the Aviation Science Funds of China
文摘A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.
