Traditional Power System State Estimation Research Articles

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.

A Belief Propagation Based Power Distribution System State Estimator

The most popular method used in traditional power system state estimation is the Maximum Likelihood Estimation (MLE). It assumes the state of the system is a set of deterministic variables and determines the most likely state via error included interval measurements. In the distribution system, the measurements are often too sparse to fulfill the system observability. Instead of introducing pseudomeasurements, we propose a Belief Propagation (BP) based distribution system state estimator. This new approach assumes that the system state is a set of stochastic variables. With a set of prior distributions, it calculates the posterior distributions of the state variables via real-time sparse measurements from both traditional measurements and the high resolution smart metering data.