用于弱电网互联的柔性直流输电系统双端构网型控制

针对常规跟网型控制下柔性直流输电系统不具备电网频率支撑能力、弱电网运行能力差的问题,提出了一种柔性直流输电系统的双端构网型控制策略。利用直流电容的动力学特性,将柔性直流输电系统模拟为同步电动机-连轴-同步发电机运行,使其具备良好的弱电网运行能力与电网主动支撑能力。在此基础上,设计了柔性直流输电系统的电网故障穿越策略。在PSCAD/EMTDC软件中进行仿真验证,结果表明所提柔性直流输电系统的双端构网型控制策略具备弱电网适应能力、快速潮流调节能力、电网频率主动支撑能力以及电网故障穿越能力。     

基于抑制环流的自适应下垂控制改进策略

在孤岛模式下的微电网中由于线路阻抗存在差异,采用传统下垂控制的逆变器不仅无法精确分配负载功率,还会产生环流。为解决该问题,提出了一种基于抑制环流的自适应下垂控制改进策略。该策略采用旋转坐标的环流来构造虚拟阻抗,通过PI控制使线路等效阻抗不断相向趋近直至相等,从而均衡分配负载功率。所提出的改进策略无需实时检测线路阻抗,也无需借助通信网络。此外,构造的虚拟阻抗不仅能有效抑制环流,还不会引起输出电压大幅跌落。仿真结果验证了改进控制策略的有效性。     

基于背景感知的相关滤波抗遮挡优化算法

针对相关滤波目标跟踪中由于目标遮挡、出视野引起的跟踪失败等问题,以背景感知相关滤波(background aware correlation filters, BACF)算法为基础,提出一种抗遮挡优化算法。首先特征提取部分融合方向梯度直方图(histogram of oriented gradient, HOG)特征与颜色名称(color name, CN)特征;其次提出层级遮挡优化算法,在目标跟踪过程中根据平均峰值相关能量(average peak to correlation energy,APCE)和最大响应峰值判断目标外观是否发生较大变化,并通过巴氏距离进一步计算目标相邻滤波器模板特征相似程度,以此判断造成目标外观变化的真正原因,进而决定是否更新模板;针对目标出视野问题,提出模板筛查策略,跟踪过程中每隔K帧对滤波器模板进行置信度考查,若目标丢失,则生成新的模板并对目标进行全局搜索重新抓取目标。通过在目标跟踪数据集OTB2015上测试,优化算法精确度与成功率分别为83.0%与78.8%,有效提高了算法性能。     

抑制间接矩阵变换器共模电压的调制策略

间接矩阵变换器在运行过程中会产生较大的共模电压,对电机系统造成严重的电磁干扰,影响系统正常运行。针对这一问题,提出一种新的空间矢量调制策略降低共模电压大小,改善输出电压性能。首先,将整流级空间矢量重新划分区域,根据参考电流矢量所在区域选择3个相邻的有效电流矢量,将有效矢量下的平均直流母线电压维持在一个恒定值,保证整流级的最大输出电压。其次,逆变级采用无零矢量调制策略,通过使用3个有效电压矢量合成所需要的输出电压,将共模电压峰值抑制到输入相电压幅值的57.7%。与传统调制策略相比,提出的调制策略不仅可以减轻系统计算负担,还可以减少系统输入输出谐波,改善输入输出波形质量。最后,通过Matlab/Simulink和实验平台搭建相应模型,仿真和实验结果验证了所提出策略的有效性。     

基于深度确定性策略梯度与模糊PID的直流微电网VRB储能系统就地层功率控制

针对直流微电网全钒液流电池(vanadium redox flow battery, VRB)储能系统在实际运行时就地控制层中的功率控制器存在时滞、精度低及抗干扰能力差等问题,提出了一种基于深度确定性策略梯度与模糊PID的功率跟踪控制策略。首先,建立VRB的等效电路模型来描述功率传输特性,并设计了由模糊PID与深度确定性策略梯度(deep deterministic policy gradient, DDPG)算法组成的复合控制器。将模糊PID作为主控制器对功率环进行控制,DDPG作为辅助控制器来补偿功率跟踪误差。然后,设计了VRB储能系统就地层功率跟踪控制器,采用麻雀搜索算法(sparrow search algorithm, SSA)对PID参数和模糊规则进行优化,并通过阶跃信号对优化后的系统输出响应进行测试。同时将分配指令功率与储能单元给定功率偏差作为数据集在DDPG中进行训练,以提高主控制器的响应速度和抗干扰能力。最后,通过在3种不同场景的算例下进行仿真,验证了控制策略的有效性及稳定性。结果表明：所提控制策略在电池充放电时,能够快速地跟踪到功率指令值;实时跟踪时,跟踪功率值与调度指令值偏差小于±2%;受到扰动时,能准确修正功率偏差,满足实际要求。     

基于多分区自适应改进果蝇算法的风光燃储微电网协调控制策略

为了使微电网控制系统中PI控制器的参数能够更好地适应可再生能源的随机性和波动性,提出了基于自适应步长的四分区多策略果蝇优化算法(fruit fly optimization algorithm, FOA)对PI参数进行实时优化。首先,以风光燃储微电网不同微源控制系统中的变换器为控制对象,建立微电网整体控制系统模型,基于此模型实时调整PI参数。然后,根据不同果蝇个体的适应度值将果蝇种群分为4个区,同时考虑4个区果蝇收敛性以及多样性的差异,设计不同的自适应更新策略。最后,采用所提算法对各微源控制过程中的PI参数进行寻优,与其他3种智能算法进行对比,验证了所提算法的可行性和优越性。仿真结果表明,所提算法可以使系统变换器响应速度更快,输出更加稳定。     

An automatic docking method for large-scale sections based on real-time pose measuring and assembly deviation control

Aiming at the problem of poor accuracy consistency of large sections’ docking assembly, an automatic docking method using multiple laser trackers to measure the position and posture of the docking sections in real time was proposed. In the solution of the pose of the docking section, real-time pose measurement of the docking section was realized by establishing a global coordinate system and a coordinate fusion method of three or more laser trackers. In the automatic control of the docking process, the real-time communication protocol and the circular negative feedback control strategy of measurement-adjustment-re-measurement are adopted, and the fully-automated docking of large sections is realized. Finally, an experimental verification system was set up, and the docking of the large-scale section reduction models was realized under the requirements of docking accuracy, and the effectiveness of the automatic docking scheme was successfully verified.     

Phase unwrapping based on deep learning in light field fringe projection 3D measurement

Phase unwrapping is one of the key roles in fringe projection three-dimensional (3D) measurement technology. We propose a new method to achieve phase unwrapping in camera array light filed fringe projection 3D measurement based on deep learning. A multi-stream convolutional neural network (CNN) is proposed to learn the mapping relationship between camera array light filed wrapped phases and fringe orders of the expected central view, and is used to predict the fringe order to achieve the phase unwrapping. Experiments are performed on the light field fringe projection data generated by the simulated camera array fringe projection measurement system in Blender and by the experimental 3×3 camera array light field fringe projection system. The performance of the proposed network with light field wrapped phases using multiple directions as network input data is studied, and the advantages of phase unwrapping based on deep learning in light filed fringe projection are demonstrated.     

Aero-optic imaging deviation prediction based on ISSA-ELM

When the aircraft is moving at high speed in the atmosphere, aero-optical imaging deviation will appear due to the influence of aero-optical effect. In order to achieve real-time compensation during the flight of the aircraft, it is necessary to analyze and predict the obtained imaging deviation data. In order to improve the search speed and accuracy of the prediction algorithm and the ability to jump out of local optimum, in this paper, an improved sparrow search algorithm optimized extreme learning machine (ISSA-ELM) neural network model is proposed to predict the aero-optical imagine deviation. Finally, the performance of ISSA-ELM, ELM neural network and SSA-ELM neural network was tested. The results showed that compared with ELM and SSA-ELM algorithms, the convergence speed of ISSA-ELM was significantly enhanced, and the accuracy of data prediction was also significantly improved.     

Progressive spatial-angular feature enhancement network for light field image super-resolution

The intensity and direction of the light field (LF) can be recorded simultaneously by using LF cameras. However, since LF cameras sacrifice spatial resolution for higher angular resolution, the images acquired by LF cameras tend to have low spatial resolution. Therefore, LF image super-resolution (SR) has become an integral part of LF studies. Many existing LF image SR methods fail to fully utilize angular and spatial information due to only using partial sub-aperture images (SAIs). In this paper, we propose a progressive spatial-angular feature enhancement network (PSAFENet) to deal with the problem of missing information in LF image SR. Specifically, we first extract the spatial features of SAIs, the spatial and angular features contained in the macro-pixel images (MacPIs) by three different feature extraction modules. Then, these features are fed into a spatial-angular feature enhancement (SAFE) module to perform enhancement of spatial-angular information on the SAIs. To improve the reconstruction accuracy, we also use the information multi-distillation block (IMDB) to remove the redundant information before upsampling. Our network can well merge the angular and spatial information into each SAI, which facilitates the reconstruction of the LF images. Experimental results on five public datasets show that the proposed PSAFENet method outperforms existing methods in both qualitative and quantitative comparisons.