The Ocean 4A scatterometer, expected to be launched in 2024, is poised to be the world’s first spaceborne microwave scatterometer utilizing a digital beamforming system. To ensure high-precision measurements and perf...The Ocean 4A scatterometer, expected to be launched in 2024, is poised to be the world’s first spaceborne microwave scatterometer utilizing a digital beamforming system. To ensure high-precision measurements and performance sta-bility across diverse environments, stringent requirements are placed on the dynamic range of its receiving system. This paper provides a detailed exposition of a field-programmable gate array (FPGA)-based automatic gain control (AGC) design for the spaceborne scatterometer. Implemented on an FPGA, the algo-rithm harnesses its parallel processing capabilities and high-speed performance to monitor the received echo signals in real time. Employing an adaptive AGC algorithm, the system gene-rates gain control codes applicable to the intermediate fre-quency variable attenuator, enabling rapid and stable adjust-ment of signal amplitudes from the intermediate frequency amplifier to an optimal range. By adopting a purely digital pro-cessing approach, experimental results demonstrate that the AGC algorithm exhibits several advantages, including fast con-vergence, strong flexibility, high precision, and outstanding sta-bility. This innovative design lays a solid foundation for the high-precision measurements of the Ocean 4A scatterometer, with potential implications for the future of spaceborne microwave scatterometers.展开更多
The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning co...The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.展开更多
针对突发式无线通信自动增益控制(automatic gain control,AGC)存在的器件非线性、响应时延及调制峰均比问题,提出一种基于模糊控制和比例微分控制的快速AGC控制方法。设计系统结构,分析AGC控制的影响因素,进行模糊控制粗调和PD控制精调...针对突发式无线通信自动增益控制(automatic gain control,AGC)存在的器件非线性、响应时延及调制峰均比问题,提出一种基于模糊控制和比例微分控制的快速AGC控制方法。设计系统结构,分析AGC控制的影响因素,进行模糊控制粗调和PD控制精调,给出软件流程,并在常温环境下进行系统实验。实验结果表明:相比传统AGC方案,该方案可在大动态范围实现快速收敛。展开更多
针对AGC控制中火电机组响应时滞长、机组爬坡速率低的问题,提出了一种基于模糊控制策略的电池储能系统(Battery Energy Storage System,BESS)辅助AGC调频方法。该方法以区域控制偏差(Area Control Error,ACE)及其变化率作为模糊控制器...针对AGC控制中火电机组响应时滞长、机组爬坡速率低的问题,提出了一种基于模糊控制策略的电池储能系统(Battery Energy Storage System,BESS)辅助AGC调频方法。该方法以区域控制偏差(Area Control Error,ACE)及其变化率作为模糊控制器的输入量,BESS的参考功率变化量作为输出量,根据系统的运行状态调节BESS输出功率,辅助火电机组改善电网的动态调频性能。基于Matlab/Simulink平台的仿真结果表明,BESS能够迅速响应负荷扰动,减小了系统频率偏差和联络线功率偏差,降低了系统的超调作用,有助于提高电网AGC调频能力和增强系统的稳定性。展开更多
文摘The Ocean 4A scatterometer, expected to be launched in 2024, is poised to be the world’s first spaceborne microwave scatterometer utilizing a digital beamforming system. To ensure high-precision measurements and performance sta-bility across diverse environments, stringent requirements are placed on the dynamic range of its receiving system. This paper provides a detailed exposition of a field-programmable gate array (FPGA)-based automatic gain control (AGC) design for the spaceborne scatterometer. Implemented on an FPGA, the algo-rithm harnesses its parallel processing capabilities and high-speed performance to monitor the received echo signals in real time. Employing an adaptive AGC algorithm, the system gene-rates gain control codes applicable to the intermediate fre-quency variable attenuator, enabling rapid and stable adjust-ment of signal amplitudes from the intermediate frequency amplifier to an optimal range. By adopting a purely digital pro-cessing approach, experimental results demonstrate that the AGC algorithm exhibits several advantages, including fast con-vergence, strong flexibility, high precision, and outstanding sta-bility. This innovative design lays a solid foundation for the high-precision measurements of the Ocean 4A scatterometer, with potential implications for the future of spaceborne microwave scatterometers.
文摘为应对新能源机组随机波动导致的外送断面过载约束复杂多变的问题,提出一种计及新能源特性的自动发电控制(automatic generation control,AGC)断面越限预防和校正控制方法。首先,提出计及新能源特性、涵盖主站-厂站-机组三级架构的AGC断面功率越限控制模型;其次,基于潮流转移比矩阵快速计算N-1故障后线路负载率矩阵,快速评估AGC断面功率越限风险;最后,推导线路N-1故障后负载率相对发电机组出力的灵敏度,明确不同机组功率调节对断面功率的差异化影响,以新能源机组消纳最大、AGC机组调节量最小为控制原则,提出基于线路负载率灵敏度的AGC断面越限校正控制方法。在PSD Power Tools中搭建实际电网仿真算例验证了所提方法的正确性和有效性。
基金Project(51074051)supported by the National Natural Science Foundation of China
文摘The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.
文摘针对突发式无线通信自动增益控制(automatic gain control,AGC)存在的器件非线性、响应时延及调制峰均比问题,提出一种基于模糊控制和比例微分控制的快速AGC控制方法。设计系统结构,分析AGC控制的影响因素,进行模糊控制粗调和PD控制精调,给出软件流程,并在常温环境下进行系统实验。实验结果表明:相比传统AGC方案,该方案可在大动态范围实现快速收敛。
文摘针对AGC控制中火电机组响应时滞长、机组爬坡速率低的问题,提出了一种基于模糊控制策略的电池储能系统(Battery Energy Storage System,BESS)辅助AGC调频方法。该方法以区域控制偏差(Area Control Error,ACE)及其变化率作为模糊控制器的输入量,BESS的参考功率变化量作为输出量,根据系统的运行状态调节BESS输出功率,辅助火电机组改善电网的动态调频性能。基于Matlab/Simulink平台的仿真结果表明,BESS能够迅速响应负荷扰动,减小了系统频率偏差和联络线功率偏差,降低了系统的超调作用,有助于提高电网AGC调频能力和增强系统的稳定性。
