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三电平逆变器的改进无模型预测电流控制 被引量:11

Improved model-free predictive current control for three-level inverter
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摘要 针对传统模型预测控制对系统模型的依赖性强、鲁棒性差的问题,提出一种应用于三相三电平中性点箝位型(NPC)逆变器的改进无模型预测电流控制方法。该方法利用当前时刻检测的负载电流和上一次计算的电流差分矢量预测下一时刻的输出电流值,其无需任何系统模型参数;通过引入计数因子,及时更新电流差分,并在下一控制周期内应用所选择的使给定代价函数最小的逆变器开关状态,有效地控制负载电流。该算法在一个采样间隔内只需对负载电流进行一次采样,但其运算量大,对系统处理器要求较高。仿真和实验结果表明:所提控制策略具有良好的稳态特性和动态响应速度,且能消除负载参数变化对控制系统稳定性的影响。 Traditional predictive control models are highly dependent on the system model,and suffer from poor robustness.To address these issues,an improved model-free prediction current control method for three-phase three-level NPC(Neutral Point Clamped)inverters is proposed.This method predicts the output current value of the next time by using the load current detected at the present time and the current difference vector calculated in the previous time period,without any system model parameters.The load current is effectively controlled by introducing a counting factor that updates the current difference in time.At the same time,the selected inverter switching state that minimizes the given cost function is applied in the next control period.The proposed method only needs to sample the load current once in one sampling interval,while it has a large amount of calculation and relatively high requirement for the system processor.The simulative and experimental results show that the proposed control strategy exhibits satisfactory steady-state characteristics and dynamic response speed,and can eliminate the negative impact of load parameters on the stability of the control system.
作者 金涛 沈学宇 苏泰新 郭敬东 JIN Tao;SHEN Xueyu;SU Taixin;GUO Jingdong(College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350116,China;State Grid Fujian Electric Power Research Institute,Fuzhou 350007,China)
出处 《电力自动化设备》 EI CSCD 北大核心 2019年第4期86-91,113,共7页 Electric Power Automation Equipment
基金 欧盟FP7国际科技合作基金资助项目(909880) 国家自然科学基金资助项目(61304260)~~
关键词 无模型预测电流控制 三电平NPC逆变器 模型参数 代价函数 model-free predictive current control three-level NPC inverter model parameters cost function
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