A distributed robust state estimation algorithm for power systems considering maximum exponential absolute value

In traditional power system state estimation application, the distribution of measurement noise is frequently assumed to be Gaussian. However, the distribution is sometimes unknown in practice and the above assumption may be wrong. In this paper, a totally distributed robust state estimation algorithm considering maximum exponential absolute value (MEAV) is proposed for multi-area power systems. The state estimation optimization problem is founded on the basis of the maximum correntropy criterion with Laplace kernel function. Considering that the cost function of MEAV is nondifferentiable, the limited case of multiple segment (MS) estimator and the matrix-splitting technique are further utilized together to carry out the MEAV based algorithm within a fully distributed framework. Each local area only utilizes local information and limited information from neighboring areas but can obtain the estimation solution respectively. The communication load is alleviated and the robustness of state estimation is increased. The proposed distributed robust MEAV based algorithm has been tested using the IEEE 14-bus, 118-bus and 300-bus systems. The simulation results demonstrate the effectiveness and robustness of the proposed distributed robust MEAV based algorithm