ANN-based hybrid state estimation and enhanced visualization of power systems

Abstract

The paper presents an artificial neural network (ANN)-based hybrid state estimator for estimating the states of a power system in the presence of conventional asynchronous as well as synchronous phasor measurements. Case studies on test systems show promising results for the ANN-based estimator. The paper also presents methodologies to enhance the visualization of the power system during the intervals between successive outputs of the conventional state estimator. The ANN-based state estimators trained with measurements from phasor measurement units (PMUs) are shown to be useful for enhancing the visualization of the power system during such intervals.