Abstract
This paper proposes an inaccuracy mitigation measure to reduce the error associated with distribution line parameters identification. Additionally, it introduces the concept of positive sequence quantities for determining the line resistance, reactive inductance, and shunt admittance. The positive sequence-based analysis is required for asymmetrical related studies such as unbalanced fault analysis. The paper, also, includes the consideration of noisy distribution networks. It compares the performance of three line parameters identification techniques by using different statistical measures. A total of 12,960 different case studies are simulated and analyzed under six main loading scenarios and four categories with changing line parameters. The line parameters are calculated online using voltage and current signals obtained from phasor measurement units (PMUs) placed at the line two terminals. Finally, the study outcomes and the associated recommendations have been summarized for future works considerations.
Highlights
Distribution line (DL) parameters identification forms the basis for distribution power system studies, including dynamic and transient stabilities, state estimate, protection setting, etc. e common practice in the industry, till today, is to determine the parameters using values from design datasheets, manufacture specification sheets, and engineer estimation. e latter could base the calculation on conductor dimensions, sag, temperature, tower geometries, and other elements. ese elements are used to identify the DL data through different mechanisms such as calculating the geometric mean radius and the geometric mean distance, denoted by GMR and GMD, respectively
Basing DL parameter estimation on offline techniques or preidentified information significantly impacts the accuracy level of the power system studies that depend on these values due to the following: (1) Conductor resistance and reactance vary with ambient conditions, conductor situation, and power flow
Single Measurement Technique. e proposed single measurement technique (SMT) aims to find DL resistance, reactive inductance and shunt admittance [12]. It uses both the phase and positive sequence of the voltage and current signals that are obtained from phasor measurement units (PMUs) at the steady state. e SMT equations are formulated as follows: ZDL1
Summary
Distribution line (DL) parameters identification forms the basis for distribution power system studies, including dynamic and transient stabilities, state estimate, protection setting, etc. e common practice in the industry, till today, is to determine the parameters using values from design datasheets, manufacture specification sheets, and engineer estimation. e latter could base the calculation on conductor dimensions, sag, temperature, tower geometries, and other elements. ese elements are used to identify the DL data through different mechanisms such as calculating the geometric mean radius and the geometric mean distance, denoted by GMR and GMD, respectively. Reference [11] presents a method to estimate distribution line parameters using only conventional SCADA measurements (voltage magnitude and power measurements) It resulted in a negligible deviation between simulation, experiment, and the actual manufacturer specifications. E proposed ohm’s formula technique (OFT) depends on the ohm’s law [12] Under this method, both phase and positive sequence voltage and current phasors are used. Is method requires only single set of voltage and current samples of the phasor voltage and current signals produced by PMUs. e developed OFT equations to calculate the DL parameters are described below: Sending (S) VS. E proposed single measurement technique (SMT) aims to find DL resistance, reactive inductance and shunt admittance [12] It uses both the phase and positive sequence of the voltage and current signals that are obtained from PMUs at the steady state. (3) Root mean square error (RMSE) is obtained by applying the square root to the MSE (4) Mean absolute percentage error (MAPE) is the average of absolute errors over the actual records
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