• DocumentCode
    483077
  • Title

    Power system state estimation based on nonlinear programming

  • Author

    Yude, Yang ; Lijun, Deng

  • Author_Institution
    Dept. of Electr. Eng., Guangxi Univ., Nanning
  • fYear
    2008
  • fDate
    17-20 Oct. 2008
  • Firstpage
    3996
  • Lastpage
    4001
  • Abstract
    State estimation base on a nonlinear programming model is presented (NLSE), which is applied the vectorization mode. We choose the L2 norm estimation as object. The nonlinear programming with equality constraint introduced slack variables, can guarantee the robust of the algorithm and dispose the convergence problem. The symmetric coefficient matrix of correction equation can be used by apply the AMD reordering algorithm and LDLT algorithm on the solution, which can speed up the calculation striking. The whole model of nonlinear state estimation applies vectorization form, so the complexity extent is simplified and both versatility and maintainability of code are improved. Numerical simulations use IEEE14, IEEE57, IEEE118, IEEE300, N1047 system to validate the correctness of the proposed model and method.
  • Keywords
    matrix algebra; nonlinear programming; power system state estimation; AMD reordering algorithm; IEEE system; LDL algorithm; nonlinear programming model; numerical simulation; power system state estimation; symmetric coefficient matrix; vectorization mode; Equations; Iterative algorithms; Mathematical model; Matrix converters; Power measurement; Power system measurements; Power system modeling; Power systems; Sparse matrices; State estimation; Newton Algorithm; Reordering algorithm; Sparse technology; State estimation; Vectorization mode; Weighted least squares (WLS) state estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Machines and Systems, 2008. ICEMS 2008. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-3826-6
  • Electronic_ISBN
    978-7-5062-9221-4
  • Type

    conf

  • Filename
    4771481