Transmission line parameter identification using PMU measurements

TL;DR

This paper introduces a new linear estimation method for more accurate transmission line parameter identification using noisy PMU data, applicable to various line configurations and load conditions.

Contribution

A novel linear estimation approach that improves transmission line parameter estimation accuracy under noise and untransposed line conditions.

Findings

The proposed method outperforms existing algorithms in noisy environments.

It accurately estimates positive, negative, and zero sequence parameters.

Validation through ATP simulations confirms its effectiveness.

Abstract

Accurate knowledge of transmission line (TL) impedance parameters helps to improve accuracy in relay settings and power flow modeling. To improve TL parameter estimates, various algorithms have been proposed in the past to identify TL parameters based on measurements from Phasor Measurement Units (PMUs). These methods are based on the positive sequence TL models and can generate accurate positive sequence impedance parameters for a fully-transposed TL when measurement noise is absent; however these methods may generate erroneous parameters when the TLs are not fully transposed or when measurement noise is present. PMU field-measure data are often corrupted with noise and this noise is problematic for all parameter identification algorithms, particularly so when applied to short transmission lines. This paper analyzes the limitations of the positive sequence TL model when used for parameter estimation of TLs that are untransposed and proposes a novel method using linear estimation theory to identify TL parameters more reliably. This method can be used for the most general case: short or long lines that are fully transposed or untransposed and have balanced or unbalance loads. Besides the positive or negative sequence impedance parameters, the proposed method can also be used to estimate the zero sequence parameters and the mutual impedances between different sequences. This paper also examines the influence of noise in the PMU data on the calculation of TL parameters. Several case studies are conducted based on simulated data from ATP to validate the effectiveness of the new method. Through comparison of the results generated by this novel method and several other methods, the effectiveness of the proposed approach is demonstrated.