并网变换器低复杂度多步递进优化虚拟矢量模型预测控制策略OA北大核心CSTPCD
A low complexity multi-step progressive optimization virtual vector model predictive control strategy for grid connected converters
为解决并网变换器有限控制集模型预测控制输出电流纹波较大的问题,提出一种并网变换器低复杂度多步递进优化虚拟矢量模型预测控制策略.首先,该方法通过构建各扇区的电压误差方程,对各扇区电压矢量误差进行量化评估.其次,在各扇区电压误差最大的位置上设计虚拟矢量,以减小电流控制误差.然后,通过所提的多步递进优化方法进一步量化分析含有虚拟矢量的各扇区电压矢量误差,并在电压误差最大的位置设计虚拟矢量,进一步减小控制误差.使用该方法进行多次优化,并确定最终优化虚拟矢量,有效减小了输出电流纹波.最后,为降低计算负担设计了扇区判断简化搜索方案,将每个大扇区分为 6 个小扇区,从而减少候选电压矢量个数,提高了系统的动态响应速度.通过对比实验验证了所提方法的有效性.
There is a problem of large output current ripple in the finite control set model predictive control of a grid-connected converter.Thus a low-complexity multi-step progressive optimization virtual vector model predictive control strategy is proposed.First,this method quantitatively evaluates the voltage vector error of each sector by constructing the voltage error equation for each sector.Secondly,virtual vectors are designed at the positions of maximum voltage errors in each sector to minimize current control errors.Then,the voltage vector errors of each sector are further quantified and analyzed by the proposed multi-step progressive optimization method,and the virtual vectors are designed at the positions of the largest voltage error,further reducing the control error.The multiple optimizations are used to determine optimal virtual vectors,reducing the current ripples efficiently.Finally,to alleviate the computational burden,a simplified sector determination search strategy is devised.Each major sector is subdivided into six smaller sectors,thereby reducing the number of candidate voltage vectors and enhancing the system's dynamic response speed.The effectiveness of the proposed method is validated by experiment.
金楠;王正伟;郭磊磊;李琰琰;吴振军
郑州轻工业大学电气信息工程学院,河南 郑州 450002
并网变换器模型预测控制多步递进优化虚拟矢量
grid-connected convertermodel predictive controlmulti-step progressive optimizationvirtual vector
《电力系统保护与控制》 2024 (016)
72-82 / 11
This work is supported by the National Natural Science Foundation of China(No.U2004166). 国家自然科学基金项目资助(U2004166);河南省优秀青年科学基金项目资助(242300421074)
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