State estimation with Neural Networks and PMU voltage measurements

Abstract

State estimation is used currently in wide area electrical systems for real time analysis. Studies have shown that in HV transmission networks, where system-wide synchronized phasor measurement units are installed, voltage angle measurements can be included in the input measurements data set, with the result of improving the estimation precision. The authors developed in previous papers a SE algorithm based on Multilayer Perceptron Artificial Neural Networks. This paper extends this research by using PMU voltage magnitude and angle measurements in the input data for the ANN estimator, and shows in a case study that the estimation precision is improved.