The merits of compressed air energy storage(CAES)include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid reg...The merits of compressed air energy storage(CAES)include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid regulation,using the traditional control mode with low accuracy can result in excess grid-connected impulse current and junction voltage.This occurs because the CAES output voltage does not match the frequency,amplitude,and phase of the power grid voltage.Therefore,an adaptive linear active disturbance-rejection control(A-LADRC)strategy was proposed.Based on the LADRC strategy,which is more accurate than the traditional proportional integral controller,the proposed controller is enhanced to allow adaptive adjustment of bandwidth parameters,resulting in improved accuracy and response speed.The problem of large impulse current when CAES is switched to the grid-connected mode is addressed,and the frequency fluctuation is reduced.Finally,the effectiveness of the proposed strategy in reducing the impact of CAES on the grid connection was verified using a hardware-in-the-loop simulation platform.The influence of the k value in the adaptive-adjustment formula on the A-LADRC was analyzed through simulation.The anti-interference performance of the control was verified by increasing and decreasing the load during the presynchronization process.展开更多
光伏电网频率调整过程中,依靠常规Smith预估控制器实现电网调频控制,对模型精度具有较强的依赖性,控制策略实施后最大频率变化率(rate of change of frequency,RoCoF)较大。因此,提出基于改进型Smith预估计器与大数据的光伏电网调频逐...光伏电网频率调整过程中,依靠常规Smith预估控制器实现电网调频控制,对模型精度具有较强的依赖性,控制策略实施后最大频率变化率(rate of change of frequency,RoCoF)较大。因此,提出基于改进型Smith预估计器与大数据的光伏电网调频逐步惯性控制方法。首先,采集历史气象数据和光伏电网运行数据,应用大数据分析领域的密度峰值聚类算法进行划分处理,再筛选相似日数据输入长短期记忆网络中,预测出未来光伏发电的功率变化;然后,依托逐步惯性控制思想,设计包含短时超发、转速恢复等多个阶段的电网调频控制策略,将模糊自适应比例-积分-微分(proportion-integration-differentiation,PID)控制器融入常规Smith预估计器,从而升级得到优化版的Smith预估计器;最后,在不受被控模型变化影响的情况下,依据预估补偿原理完成逐步惯性调频控制,并应用麻雀搜索算法求解出最优控制参数。实验结果表明:该控制方法实施后,光伏电网运行过程中最大RoCoF仅为0.086 Hz/s,有效降低了对模型精度的依赖性,保证了电力系统的稳定运行。展开更多
基金supported by National Natural Science Foundation of China(Project No.52077079).
文摘The merits of compressed air energy storage(CAES)include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid regulation,using the traditional control mode with low accuracy can result in excess grid-connected impulse current and junction voltage.This occurs because the CAES output voltage does not match the frequency,amplitude,and phase of the power grid voltage.Therefore,an adaptive linear active disturbance-rejection control(A-LADRC)strategy was proposed.Based on the LADRC strategy,which is more accurate than the traditional proportional integral controller,the proposed controller is enhanced to allow adaptive adjustment of bandwidth parameters,resulting in improved accuracy and response speed.The problem of large impulse current when CAES is switched to the grid-connected mode is addressed,and the frequency fluctuation is reduced.Finally,the effectiveness of the proposed strategy in reducing the impact of CAES on the grid connection was verified using a hardware-in-the-loop simulation platform.The influence of the k value in the adaptive-adjustment formula on the A-LADRC was analyzed through simulation.The anti-interference performance of the control was verified by increasing and decreasing the load during the presynchronization process.
文摘光伏电网频率调整过程中,依靠常规Smith预估控制器实现电网调频控制,对模型精度具有较强的依赖性,控制策略实施后最大频率变化率(rate of change of frequency,RoCoF)较大。因此,提出基于改进型Smith预估计器与大数据的光伏电网调频逐步惯性控制方法。首先,采集历史气象数据和光伏电网运行数据,应用大数据分析领域的密度峰值聚类算法进行划分处理,再筛选相似日数据输入长短期记忆网络中,预测出未来光伏发电的功率变化;然后,依托逐步惯性控制思想,设计包含短时超发、转速恢复等多个阶段的电网调频控制策略,将模糊自适应比例-积分-微分(proportion-integration-differentiation,PID)控制器融入常规Smith预估计器,从而升级得到优化版的Smith预估计器;最后,在不受被控模型变化影响的情况下,依据预估补偿原理完成逐步惯性调频控制,并应用麻雀搜索算法求解出最优控制参数。实验结果表明:该控制方法实施后,光伏电网运行过程中最大RoCoF仅为0.086 Hz/s,有效降低了对模型精度的依赖性,保证了电力系统的稳定运行。
