电源学报

有源电力滤波器混合预测无差拍电流跟踪控制

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韩伟中国电源学会会员 ¹ , 李云路 ² , 周立丽 ¹ , 陈玉芳 ¹^,³

电源学报 | 电力电子变换器建模与控制 2026,24(7): 81-93

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电源学报 |电力电子变换器建模与控制 2026 , 24 (7) : 81 -93

有源电力滤波器混合预测无差拍电流跟踪控制

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韩伟中国电源学会会员¹, 李云路², 周立丽¹, 陈玉芳¹^,³

作者信息

^1.内蒙古民族大学物理与电子信息学院，通辽　028000

^2.沈阳工业大学电气工程学院，沈阳　110870

^3.东北电力大学电气工程学院，吉林　132012

通讯作者:

周立丽（1980—　），女，通信作者，博士，讲师。研究方向：电能质量控制，功率变换与控制。E-mail：zhoulili.red@163.com。

作者简介:

韩伟（1981—　），男，中国电源学会会员，博士，副教授。研究方向：电力电子系统的建模、分析与控制，现代信号处理理论及应用。E-mail：dr_whan@163.com。

李云路（1986—　），男，博士，副教授。研究方向：智能电网稳定与控制，新能源并网技术。E-mail：liyunlu@sut.edu.cn。

陈玉芳（1981—　），女，博士，讲师。研究方向：电力系统优化运行。E-mail：139487532052@163.com。

Mixed Current Prediction-based Dead-beat Control for Active Power Filters

Wei HAN Member, CPSS¹, Yunlu LI², Lili ZHOU¹, Yufang CHEN¹^,³

Affiliations

^1.College of Physics and Electronics and Information, Inner Mongolia Minzu University, Tongliao 028000, China

^2.School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China

^3.School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

出版时间: 2026-07-30 doi: 10.13234/j.issn.2095-2805.2026.7.81

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摘要

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在工程化实现过程中，常规无差拍电流跟踪控制实际上演变为差一拍控制，导致有源电力滤波器谐波补偿性能下降。在分析系统延时对数字控制影响的基础上，综合采用自适应前向线性预测和重复预测算法提前2个采样时刻预测谐波补偿参考电流，以系统动态是否发生为依据，设定2种预测算法切换的判别条件。为了提前一拍预测逆变器输出电流，同时有效抑制测量噪声，逆变器电流预测环节同时使用待补偿谐波参考电流和输出电流历史数据作为输入。无差拍控制高度依赖主电路数学模型，对模型准确性高度敏感，抗噪声干扰能力较差，为此分析了主电路输出滤波电感和电阻参数发生偏移情况下所提预测型无差拍控制的稳定性，进一步分析了电流和电压测量噪声在系统中的传递情况。最后，对三种预测型无差拍控制进行了仿真和实验对比研究，验证了本文所提混合预测型无差拍控制算法的有效性。

关键词

无差拍控制 / 鲁棒稳定性 / 测量噪声抑制 / 混合预测 / 硬件在环测试平台

Abstract

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In the process of engineering implementation, the dead-beat current tracking control actually evolves into a one-beat lag control, which leads to the decline of harmonic compensation performance of active power filters. On the basis of analyzing the influence of system delay on digital control, adaptive forward linear prediction and repetitive prediction algorithms are prehensively used to predict the harmonic compensation reference current two sampling times in advance, and discriminant conditions for the switching of the two prediction algorithms are set based on whether the system dynamics occurs. In order to predict the output current of the inverter at one sampling times in advance and effectively suppress the measurement noise, the inverter current prediction link uses both the harmonic reference current to be compensated and the historical data of the output current as inputs. Therefore, the stability of the proposed predictive dead-beat control is analyzed when the output filter inductance and resistance parameters of the main circuit are deviated, and the transmission of current and voltage measurement noise in the system is further analyzed. Finally, simulation and experimental comparison studies on three types of predictive dead-beat control are conducted to verify the effectiveness of the mixed current prediction-based dead-beat control algorithm proposed.

Key words

Dead-beat control / robust stability / noise suppression / mixed current prediction / hardware in loop test platform

引用本文

韩伟中国电源学会会员, 李云路, 周立丽, 陈玉芳. 有源电力滤波器混合预测无差拍电流跟踪控制. 电源学报, 2026 , 24 (7) : 81 -93 . DOI: 10.13234/j.issn.2095-2805.2026.7.81

Wei HAN, Yunlu LI, Lili ZHOU, Yufang CHEN. Mixed Current Prediction-based Dead-beat Control for Active Power Filters[J]. Journal of Power Supply, 2026 , 24 (7) : 81 -93 . DOI: 10.13234/j.issn.2095-2805.2026.7.81

基金

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国家自然科学基金资助项目(51467017)
内蒙古自然科学基金资助项目(2025LHMS05012)

参考文献

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参考文献引证文献

排序方式：

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2026年第24卷第7期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.7.81

接收时间：2024-08-12
首发时间：2026-10-29
出版时间：2026-07-30

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出版历史

收稿日期：2024-08-12
修回日期：2024-10-03
录用日期：2024-10-18

基金

National Natural Science Foundation of China (NSFC)(51467017)

国家自然科学基金资助项目(51467017)

Inner Mongolia Natural Science Foundation(2025LHMS05012)

内蒙古自然科学基金资助项目(2025LHMS05012)

作者信息

^1.内蒙古民族大学物理与电子信息学院，通辽　028000

^2.沈阳工业大学电气工程学院，沈阳　110870

^3.东北电力大学电气工程学院，吉林　132012

通讯作者:

周立丽（1980—　），女，通信作者，博士，讲师。研究方向：电能质量控制，功率变换与控制。E-mail：zhoulili.red@163.com。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.7.81

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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