Abstract
Decoupled linear programming (LP) with constant Jacobian matrices is an effective tool for state estimation. The use of LP in different iterations is computationally time consuming for large power systems. This paper presents a modified decoupled linear programming technique to solve the state estimation problem. This technique is based on the sensitivity of the optimal solution to changes in the input data without the need to re-solve the problem for each new value. This technique uses LP to solve the subproblems of active and reactive powers only once and forms the inverse of the basis matrix of each subproblern from the final simplex-extended tableau. In other iterations, the right hand side of the LP which depends on the active and reactive power mismatches is changed only when the remainder matrix is not changed. The solution can be generated by direct multiplication of the inverse of the basis matrix of each sub-problem by the corresponding new power mismatch vector in a sequential manner until the optimum solution is reached. In the proposed method, both the advantages of decoupling and the bad data rejection property of LP are provided with a considerable improvement in computational efficiency.
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