摘要
针对空调用永磁压缩机在低频运行中存在转速脉动大、振动噪声高等问题,提出一种基于转矩跟踪电流误差校正的压缩机转速脉动抑制算法。首先对压缩机的周期性转速脉动进行分析,并根据转速脉动特性建立转速环控制系统模型。然后引入迭代学习算法,获得转矩跟踪电流,在此基础上,提出特征滤波函数,用来提取转矩跟踪电流基波分量,有效抑制特定次频率的转速脉动,同时引入转矩跟踪电流误差校正律,对转矩跟踪电流与参考交轴电流的误差进行校正,进一步获得前馈补偿电流,并施加到参考交轴电流中,可以消除传统迭代学习转矩跟踪电流存在的误差累积,跟踪性能好。最后,在空调压缩机实验平台上对算法进行验证,实验结果表明,所提算法可以显著抑制压缩机的转速脉动。
The speed ripple suppression algorithm of compressor motor based on torque tracking current error correction was proposed to address the issues of large torque ripple and high vibration noise in the operation of a permanent magnet compressor for air conditioning.Initially,based on the characteristics of the torque ripple,an analysis of the compressor’s periodic speed ripple was conducted.The iterative learning control approach was employed to obtain torque tracking current.On this basis,the optimized filtering function was introduced to extract the fundamental component of the torque tracking current.At the same time,the torque tracking current error correction law was introduced to correct the error between the torque tracking current and the reference torque current,and the feedforward compensation current was obtained and fed forward to the control system.The error accumulation of traditional iterative learning torque tracking current was eliminated,and the compressor speed ripple was effectively suppressed.Finally,The algorithm is validated on an experimental platform for air conditioning compressors,and the experimental results demonstrate effectively mitigating compressor torque ripple.
作者
杨哲斌
邓鎔峰
张晓军
杨家强
古汤汤
卓森庆
YANG Zhebin;DENG Rongfeng;ZHANG Xiaojun;YANG Jiaqiang;GU Tangtang;ZHUO Senqing(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;Ningbo AUX Electrical Limited Company,Ningbo 315191,China)
出处
《电机与控制学报》
EI
CSCD
北大核心
2024年第7期13-23,共11页
Electric Machines and Control
基金
浙江省自然科学基金重点项目(LCZ19E070001)
航空科学基金(20230028076008)。
关键词
压缩机系统
低频转速脉动
特征滤波函数
转矩跟踪电流误差校正律
迭代学习
compressor system
low frequency speed ripple
characteristic filter function
torque tracking current error correction law
iterative learning