Artificial Neural Networks-Based Method for Enhancing State Estimation of Grids with High Penetration of Renewables

Abstract

This paper addresses state estimation as one of the most essential mechanisms in real-time operation and control of modern power systems,and proposes a novel solution to the issue of poor network observability, commonly faced in distribution system state estimation(DSSE) characterized by an ever-increasing penetration of renewable generation. The ongoing transformation from conventional passive, one-directional power systems to active smart grids necessitates more accurate and reliable system state estimation to achieve optimal system performance. Real-time grid monitoring and control has been a routine task in transmission networks, but distribution grids cannot successfully utilize these capabilities due to different topologies, specific electrical characteristics, the low amountof available real-time measurements, as well as substantial communication effort needed to handle the data. Furthermore, with the advent of distributed generation, new types of loads and the vast surge of prosumers, a substantial amount ofdata is required to maintain system stabilityand controllability. For these reasons, reliable state estimation requires a high-quality creation process of pseudo-measurement, in addition to an efficient algorithm and an extremely accurate estimator.Thus, this paper proposes a novel framework of dynamic estimation methodology that includes the use of Artificial Neural Networks (ANN) in the pseudo-measurements generation process, utilizes Iteratively Reweighted Least Squares (IRWLS) algorithm and Schweppe-Huber Generalized Maximum Likelihood (SHGM) estimator. The efficiency and accuracy of the proposed methodology were assessed and verified on a benchmark network model.