Single Ended Fault Location Estimation for EHV Transmission Systems

A novel numerical algorithm for fault location estimation of single-phase-to-earth fault on EHV transmission lines is presented in this paper. The method is based on one-terminal voltage and current data and is used in a procedure that provides the automatic determination of faulted types and phases, rather than requires engineer to specify them. The loop and nodal equations comparing the faulted phase to non-faulted phases of multi-parallel lines are introduced in the fault location estimation models, in which source impedance of remote end is not involved. Precise algorithms of locating fault are derived. The effect of load flow and fault resistance,on the location accuracy, are effectively eliminated. The algorithms are demonstrated by digital computer simulations.     

Mathematical modelling on instability of shear fault

A study on mathematical modelling on instability of fault is reported.The fracture mechanics and fracture dynamics as a basis of the discussion,and the method of complex variable function (including the conformal mapping and approximate conformal mapping) are employed,and some analytic solutions of the problem in closed form are found.The fault body concept is emphasized and the characteristic size of fault body is introduced.The effect of finite size of the fault body and the effect of the fault propagating speed (especially the effect of the high speed) and their influence on the fault instability are discussed.These results further explain the low-stress drop phenomena observed in earthquake source.     

Sequential fault diagnosis strategy with imperfect tests considering life cycle cost

The problem of sequential fault diagnosis is to construct a diagnosis tree that can isolate the failure sources with minimal test cost. Pervious sequential fault diagnosis strategy generating algorithms only consider the execution cost at application stage, which may result in a solution with poor quality from the view of life cycle cost. Furthermore, due to the fact that uncertain information exists extensively in the real-world systems, the tests are always imperfect. In order to reduce the cost of fault diagnosis in the realistic systems, the sequential fault diagnosis problem with imperfect tests considering life cycle cost is presented and formulated in this work, which is an intractable NP-hard AND/OR decision tree construction problem. An algorithm based on AND/OR graph search is proposed to solve this problem. Heuristic search based on information theory is applied to generate the sub-tree in the algorithm. Some practical issues such as the method to improve the computational efficiency and the diagnosis strategy with multi-outcome tests are discussed. The algorithm is tested and compared with previous algorithms on the simulated systems with different scales and uncertainty. Application on a wheel momentum system of a spacecraft is studied in detail. Both the simulation and application results suggest that the cost of the diagnosis strategy can be reduced significantly by using the proposed algorithm, especially when the placement cost of the tests constitutes a large part of the total cost.     

Target Tracking Algorithm Based on Meanshift and Kalman Filter

Directed at the problem of occlusion in target tracking, a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed. The algorithm effectively combines the Meanshift algorithm with the Kalman filtering algorithm to determine the position of the target centroid and subsequently adjust the current search window adaptively according to the target centroid position and the previous frame search window boundary. The derived search window is more closely matched to the location of the target, which improves the accuracy and reliability of tracking. The environmental influence and other influencing factors on the algorithm are also reduced. Through comparison and analysis of the experiments, the modified algorithm demonstrates good stability and adaptability, and can effectively solve the problem of large area occlusion and similar interference.     

A Semi-Supervised WLAN Indoor Localization Method Based on l1-Graph Algorithm

For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.     

Dual optimization image repair algorithm based on linear structure and optimal texture

The performances of repaired image depend on the local information in the repaired area and the consistency between the repair directions with structural content. Image repair algorithm with texture information performs well in repairing seriously damaged images, but it has bad performances when the images have the abundant structure information. The dual optimization image repair algorithm based on the linear structure and the optimal texture is proposed. The algorithm uses the double-constraint sparse model to reconstruct the missed information in large area in order to improve the clarity of repaired images. After adopting the preference of Criminisi priority, the image repair algorithm of self-similarity characteristics is proposed to improve the fault and fuzzy distortion phenomena in the repaired image. The results show that the proposed algorithm has more clarity in the image texture and structure and better effectiveness, and the peak signal-to-noise ratio of the repaired images by proposed algorithm is superior to that by other algorithms.     

Fault component integrated impedance-based pilot protection scheme for EHV/UHV transmission line with thyristor controlled series capacitor(TCSC) and controllable shunt reactor(CSR)

This paper analyzes the fundamental frequency impedance characteristic of a thyristor controlled series capacitor(TCSC) and presents a novel transmission line pilot protection scheme based on fault component integrated impedance(FCII) calculated for a transmission line with TCSC and controllable shunt reactor(CSR).The FCII is defined as the ratio of the sum of the fault component voltage phasors of a transmission line with TCSC and CSR to the sum of the fault component current phasors where all the phasors are determined at both line’s terminals.It can be used to distinguish internal faults occurring on the line from external ones.If the fault is an external one the FCII reflects the line’s capacitive impedance and has large value.If the fault is an internal one on the line the FCII reflects the impedance of the equivalent system and the line and is relatively small.The new pilot protection scheme can be easily set and has the fault phase selection ability and also it is not affected by the capacitive current and the fault transition resistance.It is not sensitive to compensation level and dynamics of TCSC and CSR.The effectiveness of the new scheme is validated against data obtained in ATP simulations and Northwest China 750 kV Project.     

A Novel and Efficient Method for Iris Automatic Location

An efficient and robust iris location algorithm plays a very important role in a real iris recognition system. A novel and efficient iris automatic location method is presented in this study. It includes following two steps mainly： pu- pil location and iris outer boundary location. A digital eye image was divided into many small rectangular blocks with fixed size in the pupil location, and the block with the smallest average intensity was selected as a reference area. Then image binarization was implemented taking the average intensity of the reference area as a threshold. At last the center coordinates and radius of pupil were estimated by extending the reference area to the pupil＇s boundaries in the binary iris image. In the iris outer location, two local parts of the eye image were selected and transformed into polar coordinates from Cartesian reference. In order to detect the fainter outer boundary of the iris quickly, a novel edge detector was used to locate boundaries of the two parts. The center coordinates and radius of the iris outer boundary can be estimated using the fusion of the locating results of the two local parts and the location information of the pupil. The algorithm was tested on CASIA vl.0 and MMU vl.0 digital eye image databases and experimental results show that the proposed method has satisfying performance and good robustness.     

A Semi-Supervised WLAN Indoor Localization Method Based on 1-Graph Algorithm

For indoor location estimation based on received signal strength (RSS) in wireless local area networks (WLAN), in order to reduce the influence of noise on the positioning accuracy, a large number of RSS should be collected in offline phase. Therefore, collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper, the traditional semi-supervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed, and the result shows that the k-NN or ε-NN graph are sensitive to data noise, which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem, it proposes a 1-graph-algorithm-based semi-supervised learning (LG-SSL) indoor localization method in which the graph is built by 1-norm algorithm. In our system, it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase, and then uses LG-SSL to estimate user’s location in online phase. Extensive experimental results show that, benefit from the robustness to noise and sparsity of 1-graph, LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.     

Early Sensor Fault Detection Based on PCA and Clustering Analysis

This paper proposes a novel scoring index for the early sensor fault detection in order to make full use of massive archived spacecraft telemetry data.The early detection of sensor faults is made by using the index constructed by the K-means algorithm and PCA model.The sensor fault detection includes the learning phase and monitoring phase.The amplitude of sensor fault has been always increasing when the performance of sensors deteriorates during a period.The proposed index can detect the smaller sensor faults than the squared prediction error（ SPE） index which means it can discover the sensor faults earlier than the later.The simulation results demonstrate the effectiveness and feasibility of the proposed index which can decrease the check-limit as much as 40% than SPE in the same magnitude of bias sensor fault.     

超高压长线路故障测距研究

提出了一种适用于超高压长线路的故障测距方法。该方法基于线路分布参数模型,根据正序故障分量电压沿线分布规律,通过搜索迭代将故障点界定在一段短线路上,从而将分布参数长线测距转化为集中参数短线测距。该方法理论上不受故障过渡电阻的影响,不要求双端数据同步采样。采用反映各种故障的正序分量,不需事先确定故障相别。ATP仿真显示该方法有较高的精度。     

单端行波测距新算法的研究

传统的单端测距算法需要考虑时间和行波波速两个因素,本文在原有的单端测距算法的基础上消去了行波波速参数,提出了一种不受行波波速影响的的单端测距新算法,与之前的单端行波测距算法相比该算法不受行波波速的影响的优点,而且计算方便、精确.经过ATP对该算法进行仿真验证,仿真结果证明了该算法的实用性.用该算法在超高压长距离输电线路...     

基于暂态电流的故障选相原理的探讨

在分析故障相和非故障相的暂态电流特征的基础上,提出一种基于暂态电流的故障选相原理。该原理采用小波变换有效地提取了暂态电流特征,从而实现可靠选相。仿真结果表明,该原理不受故障类型、故障时刻、故障过渡电阻、故障位置的影响。     

T型线路行波故障测距的新方法

综合利用T型线路的三端电气量进行故障定位,提出了故障分支判别的判据以及确定故障点精确位置的方法。利用小波变换和模极大值理论对提取的行波进行分析,得到行波波头到达的准确时间。利用行波到达各测量端的时间差判断故障分支,在此基础上利用包含故障分支的初步测距值求取最终测距结果。PSCAD仿真表明,该方法可准确判断T型线路的故障分支,测距误差较小,能够满足实际需要。     

基于分布参数模型的互联电力系统输电线路故障定位

提出了一种适用于含n母线的通用互联电力系统架空输电线路单端故障定位方法。对故障输电线路采用高精确度的分布参数模型，而对电力系统则采用考虑其内部连接特性的二端口戴维宁等效网络模型，从而实现较高的故障定位精度。利用MATLAB/simulink仿真平台模拟11母线互联电力系统，获得暂态故障数据并进行了测试。测试结果表明，对各种故障条件下的故障距离估计精度高，对近端母线阻抗误差敏感，而对远端母线阻抗误差不敏感。     

基于非故障相暂态电流的小电流接地系统故障选线的研究

针对小电流接地系统单相接地故障选线的研究现状,提出基于非故障相暂态电流的单相接地故障选线的方法。该方法利用小波变换分析非故障相暂态电流,通过对暂态电流模极大值数值的比较实现故障线路的选择。ATP仿真分析的结果表明,该方法准确可靠。     

基于故障行波方向的电缆一架空线混合线路自适应重合闸控制研究

提出一种基于故障行波方向的电缆一架空线混合线路自适应重合闸控制方法．通过在电缆与架空线连接点处安装行波传感器提取暂态电流行波方向信息,计算故障区段,如果故障在电缆上则断开重合闸出口压板,反之,则闭合重合闸出口压板．理论分析和仿真结果表明,该方法能识别混合线路连接点附近故障时的故障区段,无故障保护死区,可靠性高．     

架空-电缆混合线路双端行波故障测距算法

配电线路发生故障时,及时准确的故障点定位对于保障电力系统的安全可靠性至关重要.利用基于时间中点的双端行波故障测距算法,解决了传统双端行波测距不适于波速不连续的架空-电缆混合配电线路的问题,实现了架空-电缆混合配电线路的故障定位.     

电缆-架空线混合线路故障行波定位及自适应重合闸控制

提出一种基于预值查表故障搜索算法的混合线路双端行波故障定位方法．基于各个电缆一架空线连接点产生的行波到达线路两端的时间差建立故障区段预置表,利用故障时初始行波波头到达线路两端的时间差查表搜索故障区段,根据故障区段进行重合闸控制,如果故障在电缆上则闭锁重合闸,反之,则开放重合闸;并根据双端行波定位公式计算出故障点位置．EMTP仿真结果表明,提出的行波定位方法可准确定位故障点位置,误差小于50m,且不受故障点位置、故障类型及过渡电阻的影响,可精确实现重合闸,具有较强的实用性．