摘要
在中高压大功率PWM整流器中,降低整流器功率器件的开关频率能够显著降低系统工作过程中的功率损耗,然而低开关频率运行将导致整流器网侧电流畸变率升高,电能质量下降,在电网电压不平衡时这种现象更为突出。针对上述问题,提出一种基于顺序模型预测控制的低开关频率运行方法。该方法结合瞬时功率补偿策略,通过对不平衡电网条件下整流器的功率分析获得功率参考值,采用顺序模型预测控制思想,设计双评价函数在降低整流器开关频率的同时降低了网侧电流畸变率,减小了有功功率波动。该方法可以在两相静止坐标系下实现,结构简单,计算量小,且取消了传统模型预测控制中的权重因子。仿真和实验结果验证了理论研究和所提方法的有效性和可行性。
The switching frequency of a high voltage PWM rectifier should be limited to reduce the power loss of converters.However,low switching frequency operation will incur deteriorated grid-side current performance and reduce power quality,especially under unbalanced grid-side voltage conditions.To mitigate aforementioned issues,this paper proposes a sequential model predictive control based low switching frequency method.The proposed solution takes instantaneous power compensation algorithm into consideration,calculates the power reference value by analyzing the power of the rectifier under unbalanced conditions.Besides,the sequential model predictive control is embedded into the proposed design,the dual cost functions are designed to reduce the distortion of grid-side currents and active power ripples while remaining low switching frequency.The proposed design,which is realized in the stationary frame,has the merits of simple control structure and reduced computational burden,and eliminates the weighting factors in the conventional control method.Finally,the comprehensive simulation results and experiment results are presented to confirm the theoretical study and the effectiveness of the proposed method.
作者
魏昂
王丹
武文杰
王浩亮
刘陆
彭周华
WEI Ang;WANG Dan;WU Wenjie;WANG Haoliang;LIU Lu;PENG Zhouhua(School of Marine Electrical Engineering,Dalian Maritime University,Dalian 116026,Liaoning Province,China;School of Marine Engineering,Dalian Maritime University,Dalian 116026,Liaoning Province,China)
出处
《电网技术》
EI
CSCD
北大核心
2021年第7期2700-2708,共9页
Power System Technology
基金
国家自然科学基金项目(61673081,51979020,51909021)
中央高校基本科研业务费专项基金(3132019319)。
关键词
PWM整流器
不平衡电网
低开关频率
顺序模型预测控制
PWM rectifier
unbalance grid voltage
low switching frequency
sequential model predictive control