Bad data correction in harmonic state estimation for power distribution systems: an approach based on generalised pattern search algorithm

Abstract

This paper presents a novel methodology for bad data correction in harmonic state estimation for power distribution systems. An optimisation model is formulated considering an objective function to be minimised based on the weighted least squares. Inequality constraints are incorporated to the problem for those buses which are not monitored in real time by any dedicated meter, being their corresponding active and reactive powers considered between upper and lower bounds in order to provide supplementary information about the current system state. In this paper, a measurement calibration vector is introduced into the optimisation model, assuming that a correction factor is associated with the measurements gathered from the network. GPSA (Generalized Pattern Search Algorithm) is used to determine the optimal values of each calibration factor to provide correct state estimation results. A 69-bus test system is used for the computational simulations considering different case studies with multiple bad data to be identified and corrected proving the efficiency and viability of the proposed method. The main contribution of this work is the automatic detection of bad data, identification of the corrupted measurements and correction of the bad data ensuring that the system states are estimated with errors lower than 1%.