A distributed Gauss–Newton method for distribution system state estimation

Abstract

State estimation techniques are expected to play a significant role in the management of future distribution systems. This paper proposes an efficient distributed algorithm for state estimation of distribution networks. The algorithm is designed to solve the sparse linear system of equations at each Gauss–Newton iteration with a multi-area architecture. According to this algorithm, the augmented matrix of the linear system is fully decomposed. Each area calculates and stores a part of the augmented matrix using only interior and border measurements. A distributed Gaussian elimination procedure is then performed, which relies solely on simple local calculation and limited data exchange between neighboring areas. The proposed scheme enables the distributed computation of the overall weighted least square estimation problem. Parallel processing abilities of area processors are fully utilized which makes the algorithm computationally efficient. Case studies performed on the unbalanced IEEE 33 and 123-bus systems demonstrate the good performance of the algorithm in terms of accuracy and efficiency.