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基于精确负载电流估计的 UPS 应用中单相交错式逆变器的无传感器模型预测控制。

Sensorless Model Predictive Control of Single-Phase Inverter for UPS Applications via Accurate Load Current Estimation.

机构信息

School of Aerospace Engineering, Xiamen University, Xiamen 361102, China.

出版信息

Sensors (Basel). 2023 Apr 4;23(7):3742. doi: 10.3390/s23073742.

Abstract

Single-phase inverters with an output LC filter, can generate low distortion output voltages, which are suitable for uninterruptible power supply (UPS) systems. The UPS system provides emergency power in the case of utility power failure, requiring high reliability and clean power. The sensorless control method is actually a soft-sensing technique, that reduces system cost, measurement-related losses, and, especially important for UPS systems, enhances the system reliability. This paper proposes a load current sensorless finite control set model predictive control (FCS-MPC) scheme for a single-phase UPS inverter. A time varying observer is proposed, which offers the accurate estimation for individual components simultaneously in periodic load current signal, without subsequent complex calculations. Compared with another two typical sensorless methods (the low-pass filter and the Kalman filter), the proposed observer-based FCS-MPC strategy has smaller load current estimation error and lower output voltage distortion, under both linear and nonlinear loads. The theoretical analysis is verified through simulation and experiment. A single-phase inverter rapid control prototype (RCP) is set up with the Speedgoat real-time target machine, to confirm the effectiveness of the system.

摘要

单相逆变器输出 LC 滤波器,可以产生低失真的输出电压,适用于不间断电源 (UPS) 系统。UPS 系统在市电故障时提供应急电源,需要高可靠性和清洁电源。无传感器控制方法实际上是一种软测量技术,可降低系统成本、测量相关损耗,特别是对 UPS 系统而言,可提高系统可靠性。本文提出了一种单相 UPS 逆变器的负载电流无传感器有限控制集模型预测控制 (FCS-MPC) 方案。提出了一种时变观测器,可在周期性负载电流信号中同时对各个分量进行精确估计,无需进行后续复杂计算。与另外两种典型的无传感器方法(低通滤波器和卡尔曼滤波器)相比,所提出的基于观测器的 FCS-MPC 策略在线性和非线性负载下具有更小的负载电流估计误差和更低的输出电压失真。通过仿真和实验验证了理论分析。使用 Speedgoat 实时目标机搭建了单相逆变器快速控制原型 (RCP),以验证系统的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/721e/10099161/043f7b8bfc35/sensors-23-03742-g001.jpg

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