  State estimation allows to monitor power networks, exploiting field
measurements to derive the most likely grid state. In the literature,
measurement errors are usually assumed to follow zero-mean Gaussian
distributions; however, it has been shown that this assumption often does not
hold. One such example is when considering pseudo-measurements. In distribution
networks, a significant amount of pseudo-measurements might be necessary, due
to the scarcity of real-time measurements. In this paper, a state estimator is
presented which allows to model measurement uncertainty with any continuous
distribution, without approximations. This becomes possible by writing state
estimation as a general maximum-likelihood estimation-based constrained
optimization problem. To realistically describe distribution networks,
three-phase unbalanced power flow equations are used. Results are presented
that illustrate the differences in accuracy and computational effort between
different uncertainty modeling methods, for the IEEE European Low Voltage Test
Feeder.
