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A mixed-integer linear programming approach for robust state estimation
In this paper, a mixed integer linear programming (MILP) formulation for robust state estimation (RSE) is proposed. By using the exactly linearized measurement equations instead of the original nonlinear ones, the existing mixed integer nonlinear programming formulation for RSE is converted to a MILP problem. The proposed approach not only guarantees to find the global optimum, but also does not have convergence problems. Simulation results on a rudimentary 3-bus system and several IEEE standard test systems fully illustrate that the proposed methodology is effective with high efficiency.
A mixed-integer linear programming approach for robust state estimation
In this paper, a mixed integer linear programming (MILP) formulation for robust state estimation (RSE) is proposed. By using the exactly linearized measurement equations instead of the original nonlinear ones, the existing mixed integer nonlinear programming formulation for RSE is converted to a MILP problem. The proposed approach not only guarantees to find the global optimum, but also does not have convergence problems. Simulation results on a rudimentary 3-bus system and several IEEE standard test systems fully illustrate that the proposed methodology is effective with high efficiency.
A mixed-integer linear programming approach for robust state estimation
Yanbo Chen (author) / Jin Ma (author)
2014
Article (Journal)
Electronic Resource
Unknown
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