Analysis of traveling wave based fault location method for distribution network with image processing

Laws of traveling wave data related to fault location for medium voltage distribution network are discussed and summarized. Given the tree structure of a distribution network, an image of nodes voltage is created combining the use of real-time traveling wave meters at all nodes of the tree. The novelty of this paper is that travelling wavefront are analyzed based on the dynamic changes of these images. Based on principle of the traditional fault location with traveling wave-based method for transmission networks, traveling wave data of fault location for medium voltage distribution networks are plotted in order to estimate propagation velocity and distance between the fault position and the reference node. The results indicate that taking advantage of the laws of data related to first wave front can improve the reliability of the fault location for medium voltage networks

Modeling and simulation of intermittent arc effects on traveling wave based fault location techniques for distribution network

With rapidly developing of the distribution networks the rate of the earth fault increases sharply. Aiming to fault location for distribution networks, many techniques are proposed and applied in distribution networks throughout the world. However, until now the technology for precise fault point location has not been successfully implemented in engineering practice. Traveling wave based methods as common techniques are widely applied in transmission line protection for locating fault point. However, these methods face severe challenge in fault location for distribution networks. The main reason is that the intermittent arc fault easily results in failure of detecting inceptive travelling wave and this intermittent arc is a common earth fault in distribution networks compared with transmission networks. In this paper, a simplified distribution line is built by making reference to the two parallel lossless transmission lines system. Then, the intermittent arc effects on traveling wave based method are modeled and discussed. Finally, the reason why these travelling wave based methods are hard to locate fault point precisely is illustrated

FDTD analysis of transient fault induced travelling-wave propagation for multi-branch distribution networks

Many methods are available to analyze the process of the travelling-wave propagation. Among these methods, the Finite Difference Time Domain (FDTD) method has a distinct advantage in calculating dynamic process of the travelling wave propagation in the time domain and is thus applied to the field of power system protection for researching transient fault induced travelling-wave propagation. The novelty of this paper is that the attenuation law of the traveling wave signal affected by the fork junction in the multi-branch distribution network is summarized and the cause of failure in the fault location based on the incipient travelling wave front method in distribution networks is found

Selection-based dictionary learning for sparse representation in visual tracking

This dissertation describes a novel selection-based dictionary learning method with a sparse representation to tackle the object tracking problem in computer vision. The sparse representa- tion has been widely used in many applications including visual tracking, compressive sensing, image de-noising and image classification, and learning a good dictionary for the sparse rep- resentation is critical for obtaining high performance. The most popular existing dictionary learning algorithms are generalized from K-means, which compute the dictionary columns to minimize the overall target reconstruction error iteratively. For better discriminative capability to differentiate target-object (positive) from background (negative) data, a class of dictionary algorithms has been developed to learn the dictionary from both the positive and the negative data. However, these methods do not work well for visual tracking in a dynamic environment in which the background can change considerably between frames in a non-linear way. The background cannot be modeled statically with the usual linear models. In this tdissertation, I report on the development of a selection-based dictionary learning algorithm (K-Selection) that constructs the dictionary by choosing its columns from the training data. Each column is the most representative basis for the whole dataset, which also has a clear physical meaning. With locality-constraints, the subspace represented by the learned dictionary is not restricted to the training data alone, and is also less sensitive to outliers. The sparse representation based on this dictionary learning method supports a more robust tracker trained on the target-object data alone. This is because the learned dictionary has more discriminative power and can better distinguish the object from the background clutter. By extending the dictionary with encoded spatial information, I present a new tracking algorithm which is robust to dynamic appearance changes and occlusions. The performance of the proposed algorithms have been validated for several challenging visual tracking applications through a series of comparative experiments.Ph. D.Includes bibliographical referencesby Baiyang Li