  Accurate knowledge of transmission line parameters is essential for a variety
of power system monitoring, protection, and control applications. The use of
phasor measurement unit (PMU) data for transmission line parameter estimation
(TLPE) is well-documented. However, existing literature on PMU-based TLPE
implicitly assumes the measurement noise to be Gaussian. Recently, it has been
shown that the noise in PMU measurements (especially in the current phasors) is
better represented by Gaussian mixture models (GMMs), i.e., the noises are
non-Gaussian. We present a novel approach for TLPE that can handle non-Gaussian
noise in the PMU measurements. The measurement noise is expressed as a GMM,
whose components are identified using the expectation-maximization (EM)
algorithm. Subsequently, noise and parameter estimation is carried out by
solving a maximum likelihood estimation problem iteratively until convergence.
The superior performance of the proposed approach over traditional approaches
such as least squares and total least squares as well as the more recently
proposed minimum total error entropy approach is demonstrated by performing
simulations using the IEEE 118-bus system as well as proprietary PMU data
obtained from a U.S. power utility.
