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Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach
Compared with relational database (RDB), graph database (GDB) is a more ...
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Parallel Betweenness Computation in Graph Database for Contingency Selection
Parallel betweenness computation algorithms are proposed and implemented...
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Graph Computing based Distributed State Estimation with PMUs
Power system state estimation plays a fundamental and critical role in t...
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Fast Grid Splitting Detection for N-1 Contingency Analysis by Graph Computing
In this study, a graph-computing based grid splitting detection algorith...
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A High-Performance Energy Management System based on Evolving Graph
As the fast growth and large integration of distributed generation, rene...
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Exploration of Bi-Level PageRank Algorithm for Power Flow Analysis Using Graph Database
Compared with traditional relational database, graph database, GDB, is a...
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Graph Computing based Fast Screening in Contingency Analysis
During last decades, contingency analysis has been facing challenges fro...
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Exploration of Graph Computing in Power System State Estimation
With the increased complexity of power systems due to the integration of smart grid technologies and renewable energy resources, more frequent changes have been introduced to system status, and the traditional serial mode of state estimation algorithm cannot well meet the restrict time-constrained requirement for the future dynamic power grid, even with advanced computer hardware. To guarantee the grid reliability and minimize the impacts caused by system status fluctuations, a fast, even SCADA-rate, state estimator is urgently needed. In this paper, a graph based power system modeling is firstly explored and a graph computing based state estimation is proposed to speed up its performance. The power system is represented by a graph, which is a collection of vertices and edges, and the measurements are attributes of vertices and edges. Each vertex can independently implement local computation, like formulations of the node-based H matrix, gain matrix and righthand-side (RHS) vector, only with the information on its connected edges and neighboring vertices. Then, by taking advantages of graph database, these node-based data are conveniently collected and stored in the compressed sparse row (CSR) format avoiding the complexity and heaviness introduced by the sparse matrices. With communications and synchronization, centralized computation of solving the weighted least square (WLS) state estimation is completed with hierarchical parallel computing. The proposed strategy is implemented on a graph database platform. The testing results of IEEE 14-bus, IEEE 118-bus systems and a provincial system in China verify the accuracy and high-performance of the proposed methodology.
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