Neural-Network-based State Estimation: the effect of Pseudo- measurements

IEEE International Symposium on Industrial Electronics (ISIE), pp. 1–6

Abstract

This work wants to contribute to the current research effort of exploiting deep learning models to tackle the problem of state estimation in power systems. In this contribution, we propose first a methodology for generating data-sets in case of few measurements available. The main body of the work investigates the design of artificial neural network with an extensive study on the input-layer, which plays a crucial role in robustness of the estimator. The aim is to compare performances between models which use additional pseudo-measurements in input and those which do not. Simulation results carried out on the IEEE 95-bus system and low voltage to-bus test network support the conclusions of the proposed framework.

Authors 4

1. Andrea Bragantini Aachen Aachen Institute for Advanced Study in Computational Engineering Science (AICES)

   Aachen Institute for Advanced Study in Computational Engineering Science · RWTH Aachen University

   Affiliation as printed

   Aachen Institute for Advanced Study in Computational Engineering Science, Aachen, Germany

2. Davide Baroli Aachen Aachen Institute for Advanced Study in Computational Engineering Science (AICES)

   Aachen Institute for Advanced Study in Computational Engineering Science · RWTH Aachen University

   Affiliation as printed

   Aachen Institute for Advanced Study in Computational Engineering Science, Aachen, Germany

3. Andrés Felipe Posada-Moreno Aachen Institut for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institut for Data Science in Mechanical Engineering, Aachen, Germany

4. Andrea Benigni Aachen Chair of Methods for Simulation of Energy Systems

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Chair of Methods for Simulation of Energy Systems, RWTH Aachen University

   Institute of Energy and Climate Research, Forschungszentrum Juelich GmbH, Juelich, Germany