Estimation of Impedance and Susceptance Parameters of a 3-Phase Cable System Using PMU Data

Ravi Shankar Singh, Helko van den Brom, Stanislav Babaev, Sjef Cobben and Vladimir Ćuk
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Ravi Shankar Singh: Electrical Energy Systems Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Helko van den Brom: VSL, 2629 JA Delft, The Netherlands
Stanislav Babaev: Electrical Energy Systems Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Sjef Cobben: Electrical Energy Systems Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Vladimir Ćuk: Electrical Energy Systems Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

Energies, 2019, vol. 12, issue 23, 1-22

Abstract: This paper proposes a new regression-based method to estimate resistance, reactance, and susceptance parameters of a 3-phase cable segment using phasor measurement unit (PMU) data. The novelty of this method is that it gives accurate parameter estimates in the presence of unknown bias errors in the measurements. Bias errors are fixed errors present in the measurement equipment and have been neglected in previous such attempts of estimating parameters of a 3-phase line or cable segment. In power system networks, the sensors used for current and voltage measurements have inherent magnitude and phase errors whose measurements need to be corrected using calibrated correction coefficients. Neglecting or using wrong error correction coefficients causes fixed bias errors in the measured current and voltage signals. Measured current and voltage signals at different time instances are the variables in the regression model used to estimate the cable parameters. Thus, the bias errors in the sensors become fixed errors in the variables. This error in variables leads to inaccuracy in the estimated parameters. To avoid this, the proposed method uses a new regression model using extra parameters which facilitate the modeling of present but unknown bias errors in the measurement system. These added parameters account for the errors present in the non- or wrongly calibrated sensors. Apart from the measurement bias, random measurement errors also contribute to the total uncertainty of the estimated parameters. This paper also presents and compares methods to estimate the total uncertainty in the estimated parameters caused by the bias and random errors present in the measurement system. Results from simulation-based and laboratory experiments are presented to show the efficacy of the proposed method. A discussion about analyzing the obtained results is also presented.

Keywords: distribution grid monitoring; cable temperature estimation; cable parameter estimation; PMU application in distribution grid; metrology in smart grids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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