Recursive Estimation of π-Line Parameters for Electric Power Distribution Grids

Alexander Prostejovsky, Oliver Gehrke, Anna Magdalena Kosek, Thomas Strasser

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Electrical models of power distribution grids are used in applications such as state estimation and Optimal Power Flow (OPF), the reliability of which depends on the accuracy of the model. This work presents an approach for estimating distribution line parameters from Remote Terminal Unit (RTU) measurements which are subject to measurement device tolerances and random noise. Building upon an earlier work which introduced a measurement tolerance compensation model, we aim to improve a) the robustness towards noisy data and b) the estimate of the parallel susceptance. For this purpose, we employ an Extended Kalman Filter (EKF) whose measurement
noise covariance matrix is modified in order to account for all noisy variables in the overdetermined system. Simulations confirm the advantages of the EKF over the previously used Least-Squares (LSQ) estimator. In the low random noise cases considered in this paper, the EKF yields a four-fold improvement over the LSQ for the parallel susceptance across all quantization ranges. For the highest levels of random and quantization noise, the EKF performs about 1.5 to 3 times better than the LSQ
for all line parameters. Furthermore, the EKF shows more consistent behavior when applied to data obtained from a laboratory distribution grid, which exhibits uncertainties that are not accounted for in the models.
Original languageEnglish
Title of host publicationProceedings of 2016 IEEE Electrical Power and Energy Conference
Number of pages6
Publication date2016
ISBN (Print)978-1-5090-1919-9
Publication statusPublished - 2016
EventIEEE Electrical Power and Energy Conference 2016 - Ottawa, Canada
Duration: 12 Oct 201614 Oct 2016


ConferenceIEEE Electrical Power and Energy Conference 2016

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