Multi-objective partition planning for multi-infeed HVDC system

The close proximity and the necessity of coordination between multiple high-voltage direct currents(HVDCs)raise the issue of grid partitioning in multi-infeed HVDC systems.A multi-objective partition strategy is proposed in this paper.Several types of relationships to be coordinated and complemented are analyzed and formulated using quantitative indices.According to the graph theory,the HVDC partition is transformed into a graph-cut problem and solved via the spectral clustering algorithm.Finally,the proposed method is validated for a practical multi-HVDC grid,confirming its feasibility and effectiveness.

An Intrusion Detection Algorithm Based on Feature Graph

With the development of Information technology and the popularization of Internet,whenever and wherever possible,people can connect to the Internet optionally.Meanwhile,the security of network traffic is threatened by various of online malicious behaviors.The aim of an intrusion detection system(IDS)is to detect the network behaviors which are diverse and malicious.Since a conventional firewall cannot detect most of the malicious behaviors,such as malicious network traffic or computer abuse,some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches.However,there are very few related studies focusing on both the effective detection for attacks and the representation for malicious behaviors with graph.In this paper,a novel intrusion detection approach IDBFG(Intrusion Detection Based on Feature Graph)is proposed which first filters normal connections with grid partitions,and then records the patterns of various attacks with a novel graph structure,and the behaviors in accordance with the patterns in graph are detected as intrusion behaviors.The experimental results on KDD-Cup 99 dataset show that IDBFG performs better than SVM(Supprot Vector Machines)and Decision Tree which are trained and tested in original feature space in terms of detection rates,false alarm rates and run time.

A cluster-autonomous partitioning algorithm in electrical power grid using Monte Carlo simulation

Power grid partitioning decomposes a large power grid into several clusters.Most of the existing partitioning methods suffer from a limitation that the buses within a cluster are severely topologically disconnected after partitioning in some cases.As a result,a cluster will inevitably be assigned to two or more power grid corporations.This assignment obstructs inner-cluster monitoring and control applications of the transmission system.To overcome the limitation,this paper proposes a multi-index power grid partitioning approach using Monte Carlo simulation guaranteeing cluster connectivity to ensure the cluster autonomy.A line-based binary coding technique is developed to ensure the cluster connectivity.Three partitioning indices are considered:the coherency,the cluster connectivity,and the number of clusters.Finally,the proposed partitioning method is applied to IEEE 9-bus system,IEEE 39-bus system and IEEE 145-bus system and compared with Fuzzy C-medoid(FCMdd)algorithm.

A grid-based clustering algorithm for wild bird distribution

Advanced satellite tracking technologies provide biologists with long-term location sequence data to understand movement of wild birds then to find explicit correlation between dynamics of migratory birds and the spread of avian influenza. In this paper, we propose a hierarchical clustering algorithm based on a recursive grid partition and kernel density estimation （KDE） to hierarchically identify wild bird habitats with different densities. We hierarchically cluster the GPS data by taking into account the following observations： 1） the habitat variation on a variety of geospatial scales; 2） the spatial variation of the activity patterns of birds in different stages of the migration cycle. In addition, we measure the site fidelity of wild birds based on clustering. To assess effectiveness, we have evaluated our system using a large-scale GPS dataset collected from 59 birds over three years. As a result, our approach can identify the hierarchical habitats and distribution of wild birds more efficiently than several commonly used algorithms such as DBSCAN and DENCLUE.

An Improved 3-D Ray Tracing Method Using Linear Traveltime Interpolation

An approach of three-dimensional seismic ray tracing is presented,which is derived from adopting two-dimensional linear traveltime interpolation（LTI）.By adjusting the forward process using the partition of grid interface,and backward step by considering more directions,the new approach is suitable for the application of three-dimensional models.The calculation results show that,with the same accuracy,the improved 3-D method is much faster than the method of traditional LTI directly applied in the three-dimensional case.