Voltage State Estimation using a Power Network Model driven AutoEncoder Neural Network

Abstract

Abstract This paper proposes a combined network model and synchrophasor data-based approach to formulate a non-linear state estimator for the reconstruction of the three-phase voltages of the nodes (buses) in an electric power network. The network model uses network topology with impartial admittance information to create a first estimate of any missing three-phase complex-valued synchrophasor voltage measurements. These first estimates along with synchrophasor measurements from other nodes in the network are used in a trained autoencoder neural network to further improve the three-phase voltage state estimation. Instead of standard voltage magnitude and voltage angle synchrophasor data, the proposed method uses real and imaginary parts of the three-phase voltages for state reconstruction to address discontinuities in voltage phase angle wrapping. The approach is illustrated on an example network and actual synchrophasor data measurements to validate performance. The illustrations show that the nonlinear state estimator that combines the network model and synchrophasor data-based approach can outperform a non-linear estimator that uses synchrophasor data only.