准确地检测失步信号并确定最佳的解列断面是终止电力系统振荡的关键。分析单机无穷大系统失稳时网络中不同部位支路相角差的变化特点,提出利用支路两端相角差作为失步解列原理的判据。对于多机系统,基于网络参数,确定了失步解列装置在...准确地检测失步信号并确定最佳的解列断面是终止电力系统振荡的关键。分析单机无穷大系统失稳时网络中不同部位支路相角差的变化特点,提出利用支路两端相角差作为失步解列原理的判据。对于多机系统,基于网络参数,确定了失步解列装置在系统中的信号检测点,并对解列断面的选取进行了探索性的分析。结果表明:失步解列装置应在网络结构脆弱、最易导致系统失稳的失步断面处检测信号;解列断面应在失稳的群间联络线对应的多个割集中选取潮流较小、构成割集的支路数较少的断面进行解列。对New England 10机系统进行了仿真分析,确定了失步解列装置的信号检测点和解列断面。展开更多
For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest p...For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.展开更多
文摘准确地检测失步信号并确定最佳的解列断面是终止电力系统振荡的关键。分析单机无穷大系统失稳时网络中不同部位支路相角差的变化特点,提出利用支路两端相角差作为失步解列原理的判据。对于多机系统,基于网络参数,确定了失步解列装置在系统中的信号检测点,并对解列断面的选取进行了探索性的分析。结果表明:失步解列装置应在网络结构脆弱、最易导致系统失稳的失步断面处检测信号;解列断面应在失稳的群间联络线对应的多个割集中选取潮流较小、构成割集的支路数较少的断面进行解列。对New England 10机系统进行了仿真分析,确定了失步解列装置的信号检测点和解列断面。
基金Projects(61203332,61203208) supported by the National Natural Science Foundation of China
文摘For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.
