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With the increased complexity of power systems due to the integration ofsmart grid technologies and renewable energy resources, more frequent changeshave been introduced to system status, and the traditional serial mode of stateestimation algorithm cannot well meet the restrict time-constrained requirementfor the future dynamic power grid, even with advanced computer hardware. Toguarantee the grid reliability and minimize the impacts caused by system statusfluctuations, a fast, even SCADA-rate, state estimator is urgently needed. Inthis paper, a graph based power system modeling is firstly explored and a graphcomputing based state estimation is proposed to speed up its performance. Thepower system is represented by a graph, which is a collection of vertices andedges, and the measurements are attributes of vertices and edges. Each vertexcan independently implement local computation, like formulations of thenode-based H matrix, gain matrix and righthand-side (RHS) vector, only with theinformation on its connected edges and neighboring vertices. Then, by takingadvantages of graph database, these node-based data are conveniently collectedand stored in the compressed sparse row (CSR) format avoiding the complexityand heaviness introduced by the sparse matrices. With communications andsynchronization, centralized computation of solving the weighted least square(WLS) state estimation is completed with hierarchical parallel computing. Theproposed strategy is implemented on a graph database platform. The testingresults of IEEE 14-bus, IEEE 118-bus systems and a provincial system in Chinaverify the accuracy and high-performance of the proposed methodology.

Exploration of Graph Computing in Power System State Estimation